Systems and methods for analyzing, interpreting, and acting on continuous glucose monitoring data

ABSTRACT

Methods and devices include automated coaching for management of glucose states by receiving a user&#39;s glucose levels using a continuous glucose monitoring (CGM) device, determining a time in range (TIR) value, determining a TIR state, receiving a glucose variability (GV) value, determining a GV state, determining a starting state based on the TIR state and the GV state, determining that the starting state corresponds to a non-ideal state, generating an optimized pathway to reach an ideal state based on one or more account vectors such as addressing self-management behavior including food, activity, and medication use. The optimized pathway may further be based on computer detection and classification of significant events of interest over time.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/206,858, filed Mar. 19, 2021, which claims the benefit of priorityto 1) U.S. Provisional Application No. 63/135,818, filed on Jan. 11,2021, 2) U.S. Provisional Application No. 62/992,385, filed on Mar. 20,2020, and 3) U.S. Provisional Application No. 62/992,409, filed on Mar.20, 2020, each of which are incorporated herein by reference in itsentirety.

TECHNICAL FIELD

The present disclosure relates generally to obtaining and processingdata to generate optimized pathways to improve the health of a user,and, in some embodiments, specifically toward optimizing glucose statesof a user via a mobile application.

INTRODUCTION

Increased healthcare costs have limited user access to appropriate care.At the same time, healthcare companies have increased provider workloadsand limited physician-user interactions. Diabetes treatment often relieson sporadic readings (e.g., glucose readings) that do not provide ampledata to effectively provide treatment options. Such readings are oftenused in isolation such that changes are recommended based on just a fewreadings. Any medical, dietary, and/or lifestyle changes recommended asa result of a given reading are therefore limited given the sparse datareceived via the sporadic readings.

The present disclosure is directed to addressing one or more of theabove-referenced challenges. The introduction provided herein is for thepurpose of generally presenting the context of the disclosure. Unlessotherwise indicated herein, the materials described in this section arenot prior art to the claims in this application and are not admitted tobe prior art, or suggestions of the prior art, by inclusion in thissection.

SUMMARY

This disclosure is directed to a computer-implemented method formanaging glucose states of a user and includes receiving the user'sglucose levels using a continuous glucose monitoring (CGM) device,determining a time in range (TIR) value of the user's glucose level,wherein the TIR value is based on an amount of time the user's glucoselevel is within a threshold band over a base time period, determining aTIR state based on the TIR value, receiving a glucose variability (GV)value based at least on the user's glucose level, wherein the GV valueis one of a standard deviation (SD) or a coefficient of variance (CV),wherein a CV indicates a variability of the user's glucose level in viewof a standard deviation of the glucose level over the base time period,determining a GV state based on the GV value, determining a startingstate based on the TIR state and the GV state, determining that thestarting state corresponds to a non-ideal state, generating an optimizedpathway to reach an ideal state based on one or more account vectors,the optimized pathway comprising one or more adjustments to the one ormore account vectors, and providing the optimized pathway to the user.

The threshold band may be between approximately 70 mg/dL and 180 mg/dL,the base time period may be 24 hours. The CV value may determined bydividing the standard deviation of the glucose level by a mean of theglucose level over the base time period. The TIR state may be a binarystate selected form one of a good TIR state or a bad TIR state. The goodTIR state may correspond to a TIR value of greater than a TIR threshold.The GV state may be a binary state selected form one of a good GV stateor a bad GV state. The good GV state may correspond to a GV value ofgreater than a GV threshold. The account vectors may comprise one ormore of glucose levels, medications, food consumption, exercise value,psycho-social parameters, or social-determinant parameters. The accountvector may comprise glucose levels based on one or more CGM eventsclassified based on a severity score. The optimized pathway is furtherbased on a user attribute, the user attribute selected from one or moreof a social attribute, medical attribute, user preference, metabolicattribute, or user demographic. The optimized pathway may comprise anincrease in one or more state improving habits and/or a decrease in oneor more state worsening habits.

This disclosure is directed to a computer-implemented method formanaging glucose states of a user and includes generating a plurality ofoptimization profiles for reaching an ideal state from a non-idealstate, the ideal state corresponding to a good time in range (TIR) stateand good a glucose variability (GV) state and the non-ideal statecomprising at least one of a bad TIR state or a bad GV state,determining a current TIR state based on a TIR value of the user'sglucose level, wherein the TIR value is based on an amount of time theuser's glucose level is within a threshold band over a base time periodand the current TIR state is one of a good TIR state or a bad TIR state,determining a current GV state being based on a GV value associated withthe user's glucose level, wherein the GV value indicates a standarddeviation (SD) of glucose levels or a coefficient of variance (CV),wherein the CV is variability of the user's glucose level in view of astandard deviation of the glucose level over the base time period,receiving one or more account vectors for the user, identifying one ofthe optimization profiles based on the one or more account vectors, theTIR state, and the CV state, identifying an optimized pathway based onthe identified optimization profile, the optimized pathway comprisingone or more adjustments to the one or more account vectors, andproviding the optimized pathway to the user.

The plurality of optimization profiles may be generated by a machinelearning model configured to receive account vectors as inputs andoutput one or more adjustments to the received account vectors. Theplurality of optimization profiles may be further generated byassociating the one or more adjustments to the received account vectorswith one or more TIR states or GV states. Each of the plurality ofoptimization profiles may correspond to a potential TIR state, apotential GV state, and the one or more potential account vectors. Oneor more user attribute may be received and one of the optimizationprofiles may be identified further based on the one or more userattributes. The CV value may be determined by dividing the standarddeviation of the glucose level by the mean of the glucose level over thebase time period.

This disclosure is also directed to a system for managing glucose levelsof a user, the system including a memory having processor-readableinstructions stored therein, a processor configured to access the memoryand execute the processor-readable instructions, which, when executed bythe processor configures the processor to perform a method. The methodincludes electronically receiving the user's glucose levels using acontinuous glucose monitoring (CGM) device configured to obtain glucosevalues using a component that penetrates a skin of the user, determininga time in range (TIR) value of the user's glucose level, wherein the TIRvalue is based on an amount of time the user's glucose level is within athreshold band over a base time period wherein the threshold band isbetween approximately 70 mg/dL and 180 mg/dL and the base time period is24 hours, determining a TIR state based on the TIR value, wherein theTIR state is selected form a good TIR state or a bad TIR state,receiving a glucose variability (GV) value based at least on the user'sglucose level, wherein the GV value is one of a standard deviation or acoefficient of variance (CV), wherein a CV indicates a variability ofthe user's glucose level in view of a standard deviation of the glucoselevel over the base time period, determining a GV state based on the GVvalue, wherein the GV state is one of a good GV state or a bad GV state,determining a starting state based on the TIR state and the GV state,determining that the starting state corresponds to a non-ideal state,detecting a CGM event based on the user's glucose levels, characterizingthe CGM event based on one or more of a multi-parameter CGMclassification or a severity and CGM event trace shape characterization,wherein the multi-parameter CGM classification comprises a glucose levelat the beginning of the CGM event, a severity, and a glucose at the endof the CGM event, generating an optimized pathway to reach an idealstate based on one or more account vectors and the characterizing theCGM event, the optimized pathway comprising one or more adjustments tothe one or more account vectors, and providing the optimized pathway tothe user. Providing the optimized pathway to the user may includeproviding context based instructions to the user based on the optimizedpathway.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate examples of the disclosure andtogether with the description, serve to explain the principles of thedisclosure.

FIG. 1 is a schematic illustration of a health management system,according to an example of the present disclosure.

FIG. 2 is a schematic illustration of a portion of the health managementsystem of FIG. 1.

FIG. 3A is a schematic illustration of another portion of the healthmanagement system of FIG. 1.

FIG. 3B is a schematic illustration of training an exemplary machinelearning model, according to an example of the present disclosure.

FIG. 4A is a continuous glucose monitoring (CGM) chart, according to anexample of the present disclosure.

FIG. 4B is a continuous glucose monitoring (CGM) report, according to anexample of the present disclosure.

FIG. 5A is a flowchart of a health management method, according to anexample of the present disclosure.

FIG. 5B is a flowchart of an exemplary health management method,according to another example of the present disclosure.

FIG. 6A is a patient state graph, according to another example of thepresent disclosure.

FIG. 6B is a patent state over time correlated to a patient state changegraph, according to another example of the present disclosure.

FIG. 6C shows three graphs of patient states over time, according toanother example of the present disclosure.

FIG. 6D shows a standard deviation graph and a coefficient of variancegraph, according to another example of the present disclosure.

FIG. 6E shows state distributions for a plurality of patents, accordingto another example of the present disclosure.

FIG. 6F shows changes in state distributions for a plurality of patents,according to another example of the present disclosure.

FIG. 6G shows glucose value changes over time and a corresponding firstderivate graph, according to another example of the present disclosure.

FIG. 7A shows a mean glucose and range chart and a variation of glucoseand range chart, according to another example of the present disclosure.

FIG. 7B shows a continuous glucose monitoring activating time andvariation chart, according to another example of the present disclosure.

FIG. 8A shows a continuous glucose monitoring chart, according toanother example of the present disclosure.

FIG. 8B shows another continuous glucose monitoring chart, according toanother example of the present disclosure.

FIG. 8C shows another continuous glucose monitoring chart, according toanother example of the present disclosure.

FIG. 8D shows another continuous glucose monitoring chart, according toanother example of the present disclosure.

FIG. 9 is another flowchart of a health management method, according toan example of the present disclosure.

FIG. 10A is a continuous glucose monitoring event visualization,according to an example of the present disclosure.

FIG. 10B is diet monitoring event visualization, according to an exampleof the present disclosure.

FIG. 11 is severity count visualization, according to an example of thepresent disclosure.

FIG. 12 is an automated alert generation chart, according to an exampleof the present disclosure.

FIG. 13A is a screenshot of an exemplary message, in accordance with anexample of the present disclosure.

FIG. 13B is another screenshot of an exemplary message, in accordancewith an example of the present disclosure.

FIG. 13C is another screenshot of an exemplary message, in accordancewith an example of the present disclosure.

FIG. 13D is a glucose computer, in accordance with an example of thepresent disclosure.

FIG. 14 is a simplified functional block diagram of a computer that maybe configured as a host server, for example, to function as healthcareprovider decision-making server, according to an example of the presentdisclosure.

An Appendix is provided herewith and includes a description withexamples of the present disclosure including experimental results.

DETAILED DESCRIPTION

Reference will now be made in detail to examples of the disclosure,which are illustrated in the accompanying drawings. Wherever possible,the same reference numbers will be used throughout the drawings to referto the same or like parts.

In the discussion that follows, relative terms such as “about,”“substantially,” “approximately,” etc. are used to indicate a possiblevariation of ±10% in a stated numeric value. It should be noted that thedescription set forth herein is merely illustrative in nature and is notintended to limit the examples of the subject matter, or the applicationand uses of such examples. Any implementation described herein asexemplary is not to be construed as preferred or advantageous over otherimplementations. Rather, as alluded to above, the term “exemplary” isused in the sense of example or “illustrative,” rather than “ideal.” Theterms “comprise,” “include,” “have,” “with,” and any variations thereofare used synonymously to denote or describe a non-exclusive inclusion.As such, a process, method, article, or apparatus that uses such termsdoes not include only those steps, structure or elements but may includeother steps, structures or elements not expressly listed or inherent tosuch process, method, article, or apparatus. Further, the terms “first,”“second,” and the like, herein do not denote any order, quantity, orimportance, but rather are used to distinguish one element from another.Moreover, the terms “a” and “an” herein do not denote a limitation ofquantity, but rather denote the presence of at least one of thereferenced item.

Healthcare and Computing Environment

FIG. 1 is a block diagram of a health management system 100, accordingto an example of the present disclosure. A user (e.g., a patient,consumer, or the like) 8 having an electronic device 19, such as amobile device, computer, medical device, or any other electronic deviceconfigured to access an electronic network 32, such as the Internet, maycommunicate with or otherwise access a mobile health (mHealth)application 1. In some examples, network 32 may include wireless orwired links, such as mobile telephone networks, Wi-Fi, LANs, WANs,Bluetooth, near-field communication (NFC), or other suitable forms ofnetwork communication. Multiple electronic devices 19 may be configuredto access electronic network 32. A user 8 may access mHealth application1 with a single account linked to multiple electronic devices 19 (e.g.,via one or more of a mobile phone, a tablet, and a laptop computer).Electronic device 19 also may include, but is not limited to, mobilehealth devices, a desktop computer or workstation, a laptop computer, amobile handset, a personal digital assistant (PDA), a cellulartelephone, a network appliance, a camera, a smart phone, a smart watch,an enhanced general packet radio service (EGPRS) mobile phone, a mediaplayer, a navigation device, a game console, a set-top box, a biometricsensing device with communication capabilities, a smart TV, or anycombination of these or other types of computing devices having at leastone processor, a local memory, a display (e.g., a monitor or touchscreendisplay), one or more user input devices, and a network communicationinterface. The electronic device 19 may include any type or combinationof input/output devices, such as a display monitor, keyboard, touchpad,accelerometer, gyroscope, mouse, touchscreen, camera, a projector, atouch panel, a pointing device, a scrolling device, a button, a switch,a motion sensor, an audio sensor, a pressure sensor, a thermal sensor,and/or microphone. Electronic devices 19 also may communicate with eachother by any suitable wired or wireless means (e.g., via Wi-Fi, radiofrequency (RF), infrared (IR), Bluetooth, Near Field Communication, orany other suitable means) to send and receive information.

mHealth application 1 may be in communication with other entities ornetworks to send and receive information. In some examples, mHealthapplication 1 may communicate with one or more applications associatedwith the user 8 such as, e.g., exercise tracking (e.g., step tracking)applications and/or other health-related applications. mHealthapplication 1 may be able to import data from the other applications toanalyze and use in generating treatment plans for the user 8. Forexample, mHealth application 1 may import activity tracking data fromanother application and use that data to identify patterns between user8 exercise and glucose values collected prior to the use of mHealthapplication 1. mHealth application 1 also may import any other suitabledata from other mobile health applications such as, e.g., bloodpressure, BMI, A1C, exercise type, exercise duration, exercise distance,calories burned, total steps, exercise date, exercise start and stoptimes, and sleep. mHealth application 1 also may export data to othermobile applications, including, e.g., other mobile health applicationshaving social or interactive features. A healthcare provider 7, such asa physician, may prescribe the application. However, it is alsocontemplated that mHealth application 1 may not require a prescription,e.g., that it may be a commercially available consumer applicationaccessible without a prescription from a digital distribution platformfor computer software. mHealth application 1 may be tailored to aspecific user 8 and may be activated in person by the user 8 by visitinga pharmacy 9 or other authorized entity. For example, the user 8 mayreceive an access code from the pharmacy that authorizes access tomHealth application 1. The user 8 may receive training on using mHealthapplication 1 by a mHealth support system 25 and/or application trainer24. mHealth application 1 may include programming 28 of various forms,such as machine learning programming algorithms 26. The user treatmentplan may include a prescription (e.g., for a drug, device, and/ortherapy), which may be dispensed by the pharmacy 9. The pharmacy 9 mayallow the refill of the prescribed product/therapy after receivingauthorization based on the user's compliance with his/her healthcaretreatment plan. The authorization may be received by the pharmacy 9 by acommunication from the application 1, via, e.g., the network 32 andvarious servers 29. Use of the drug or other medical product/therapyalso may be sent to the manufacturer 37 over the network 32 to informthe manufacturer 37 of the amount of medical product or therapy beingused by user 8. This information may assist the manufacturer 37 inassessing demand and planning supply of the medical product or therapy.The healthcare provider 7 also may receive a report based on the userinformation received by the application 1, and may update the usertreatment plan based on this information. The user's electronic medicalrecord (EMR) 14 also may be automatically updated via the network 32based on the user information, which may include electronicallytransmitted user 8 feedback on the application, received by mHealthapplication 1. Healthcare provider 7 may be any suitable healthcareprovider including, e.g., a doctor, specialist, nurse, educator, socialworker, MA, PA, or the like.

FIG. 2 is a schematic diagram of additional aspects of system 100. Forexample, the system 100 may access decision models stored on a decisionmodel database 270 via network 32. The retrieved decision models may beused for display and/or processing by one or more electronic devices 19,such as a mobile device 215, a tablet device 220, a computer (e.g., alaptop or desktop) 225, a kiosk 230 (e.g., at a kiosk, pharmacy, clinic,or hospital having medical and/or prescription information), and/or anydevice connected to network 32.

In the example shown in FIG. 2, mobile device 215, tablet 220, andcomputer 225 each may be equipped with or include, for example, a GPSreceiver for obtaining and reporting location information, e.g., GPSdata, via network 32 to and from any of servers 29 and/or one or moreGPS satellites 255.

Each of electronic devices 19, including mobile device 215, tabletdevice 220, computer 225, and/or kiosk 230, may be configured to sendand receive data (e.g., clinical information) to and from a system ofservers 29 over network 32. Each of devices 19 may receive information,such as clinical data via the network 32 from servers 29. Servers 29 mayinclude clinical data servers 240, algorithm servers 245, user interface(UI) servers 250, and/or any other suitable servers. Electronic device19 may include a user interface that is in data communication with UIserver 250 via network 32. Each server may access the decision modeldatabase 270 to retrieve decision models. Each server may includememory, a processor, and/or a database. For example, the clinical dataserver 240 may have a processor configured to retrieve clinical datafrom a provider's database and/or a patient's electronic medical record.The algorithm server 245 may have a database that includes variousalgorithms, and a processor configured to process the clinical data. TheUI server 250 may be configured to receive and process user 8 input,such as clinical decision preferences. The satellite 255 may beconfigured to send and receive information between servers 29 anddevices 19.

The clinical data server 240 may receive clinical data, such as dataregarding the user from the electronic device 19 via the network 32 orindirectly via the UI server 250. The clinical data server 240 may savethe information in memory, such as a computer readable memory.

The clinical data server 240 also may be in communication with one ormore other servers, such as the algorithm server 245 and/or externalservers. The servers 29 may include data about provider preferences,and/or user 8 health history. In addition, the clinical data server 240may include data from other users. The algorithm server 245 may includemachine learning, and/or other suitable algorithms. The algorithm server245 also may be in communication with other external servers and may beupdated as desired. For example, the algorithm server 245 may be updatedwith new algorithms, more powerful programming, and/or more data. Theclinical data server 240 and/or the algorithm server 245 may process theinformation and transmit data to the model database 270 for processing.In one example, algorithm server(s) 245 may obtain a pattern definitionin a simple format, predict several time steps in the future by usingmodels, e.g., Markov models, Gaussian, Bayesian, PCA (principalcomponent analysis), multi-variate linear or non-linear regression,and/or classification models such as linear discriminant functions,nonlinear discriminant functions, synthetic discriminant functionsrandom forest algorithms and the like, optimize results based on itspredictions, detect transition between patterns, obtain abstract dataand extract information to infer higher levels of knowledge, combinehigher and lower levels of information to understand about the user 8and clinical behaviors, infer from multi-temporal (e.g., different timescales) data and associated information, use variable order Markovmodels, and/or reduce noise over time by employing slope-based and curvesmoothing algorithms, clustering algorithms, such as k-means clustering.

Each server in the system of servers 29, including clinical data server240, algorithm server 245, and UI server 250, may represent any ofvarious types of servers including, but not limited to, a web server, anapplication server, a proxy server, a network server, or a server farm.Each server in the system of servers 29 may be implemented using, forexample, any general-purpose computer capable of serving data to othercomputing devices including, but not limited to, devices 19 or any othercomputing device (not shown) via network 32. Such a general-purposecomputer can include, but is not limited to, a server device having aprocessor and memory for executing and storing instructions. The memorymay include any type of random access memory (RAM) or read-only memory(ROM) embodied in a physical storage medium, such as magnetic storageincluding floppy disk, hard disk, or magnetic tape; semiconductorstorage such as solid-state disk (SSD) or flash memory; optical discstorage; or magneto-optical disc storage. Software may include one ormore applications and an operating system. Hardware can include, but isnot limited to, a processor, memory, and graphical UI display. Eachserver also may have multiple processors and multiple shared or separatememory components that are configured to function together within, forexample, a clustered computing environment or server farm.

FIG. 3A is another representation of a portion of system 100 showingadditional details of electronic device 19 and a server 29. Electronicdevice 19 and server 29 each may contain one or more processors, such asprocessors 301-1 and 304-1. Processors 301-1 and 304-1 each may be acentral processing unit, a microprocessor, a general purpose processor,an application specific processor, or any device that executesinstructions. Electronic device 19 and server 29 also may include one ormore memories, such as memories 301-2 and 304-2 that store one or moresoftware modules. Memories 301-2 and 304-2 may be implemented using anycomputer-readable storage medium, such as hard drives, CDs, DVDs, flashmemory, RAM, ROM, etc. Memory 301-2 may store a module 301-3, which maybe executed by processor 301-1. Similarly, memory 304-2 may store amodule 304-3, which may be executed by processor 304-1.

Electronic device 19 may further comprise one or more UIs. The UI mayallow one or more interfaces to present information to a user 8, such asa plan or intervention. The UI may be web-based, such as a web page, ora stand-alone application. The UI also may be configured to acceptinformation about a user 8, such as data inputs and user feedback. Theuser 8 may manually enter the information, or it may be enteredautomatically. In an example, the user 8 (or the user's caretaker) mayenter information such as when medication was taken or what food anddrink the user 8 consumed. Electronic device 19 also may include testingequipment (not shown) or an interface for receiving information fromtesting equipment. Testing equipment may include, for example, a bloodglucose meter, glucose meter, heart rate monitor, weight scale, bloodpressure cuff, or the like. The electronic device 19 also may includeone or more sensors (not shown), such as a camera, microphone, oraccelerometer, for collecting feedback from a user 8. In one example,the device may include a glucose meter for reading and automaticallyreporting the user's glucose levels.

Electronic device 19 also may include a presentation layer. Thepresentation layer may be a web browser, application, messaginginterface (e.g., e-mail, instant message, SMS, etc.), etc. Theelectronic device 19 may present notifications, alerts, readingmaterials, references, guides, reminders, or suggestions to a user 8 viapresentation layer. For example, the presentation layer may presentarticles that are determined to be relevant to the user 8, reminders topurchase medications, tutorials on topics (e.g., a tutorial oncarbohydrates), testimonials from others with similar symptoms, and/orone or more goals (e.g., a carbohydrate counting goal). The presentationlayer also may present information such as a tutorial (e.g., a userguide or instructional video) and/or enable communications between thehealthcare provider, and the user 8, e.g., patient. The communicationsbetween the healthcare provider, and the user 8, e.g., patient, may bevia electronic messaging (e.g., e-mail or SMS), voice, or real-timevideo. One or more of these items may be presented based on a treatmentplan or an updated treatment plan, as described later. The presentationlayer also may be used to receive feedback from a user.

The system 100 also may include one or more databases, such as adatabase 302. Database 302 may be implemented using any databasetechnology known to one of ordinary skill in the art, such as relationaldatabase technology or object-oriented database technology. Database 302may store data 302-1. Data 302-1 may include a knowledge base for makinginferences, statistical models, and/or user information. Data 302-1, orportions thereof, may be alternatively or simultaneously stored inserver 29 or electronic device 19.

System 100 can be used for a wide range of applications, including, forexample, addressing a user's healthcare, maintaining a user's finances,and monitoring and tracking a user's nutrition and/or sleep. In someimplementations of system 100, any received data may be stored in thedatabases in an encrypted form to increase security of the data againstunauthorized access and complying with HIPAA privacy, and/or otherlegal, healthcare, financial, or other regulations.

For any server or server systems 29 depicted in system 100, the serveror server system may include one or more databases. In an example,databases may be any type of data store or recording medium that may beused to store any type of data. For example, database 302 may store datareceived by or processed by server 29 including information related to auser's treatment plan, including timings and dosages associated witheach prescribed medication of a treatment plan. Database 302 also maystore information related to the user 8 including their literacy levelrelated to each of a plurality of prescribed medications.

As further disclosed herein, one or more components of the disclosedsubject matter may be implemented using a machine learning model. FIG.3B shows an example training module 310 to train one or more of themachine learning models disclosed herein. It will be understood that adifferent training module may be used to train each of the machinelearning models disclosed herein and/or a single training module 310 maybe used to train two or more machine learning models.

As shown in FIG. 3B, training data 312 may include one or more of stageinputs 314 and known outcomes 318 related to a machine learning model tobe trained. The stage inputs 314 may be from any applicable sourceincluding a healthcare provider 7, one or more servers 29, electronicdevices 19, EMR 14, an output from a step (e.g., one or more outputsfrom a step from flowchart 500 of FIG. 5A or flowchart 900 of FIG. 9,time in range (TIR) values, time above range (TAR) values, time belowrange (TBR) values, severity score, continuous glucose monitoring (CGM)classification, etc.). The known outcomes 318 may be included formachine learning models generated based on supervised or semi-supervisedtraining. An unsupervised machine learning model may not be trainedusing known outcomes 318. Known outputs 318 may include known or desiredoutputs for future inputs similar to or in the same category as stageinputs 314 that do not have corresponding known outputs.

The training data 312 and a training algorithm 320 may be provided to atraining component 330 that may apply the training data 312 to thetraining algorithm 320 to generate a machine learning model. Accordingto an implementation, the training component 330 may be providedcomparison results 316 that compare a previous output of thecorresponding machine learning model to apply the previous result tore-train the machine learning model. The comparison result 316 may beused by the training component 330 to update the corresponding machinelearning model. The training algorithm 320 may utilize machine learningnetworks and/or models including, but not limited to a deep learningnetwork such as Deep Neural Networks (DNN), Convolutional NeuralNetworks (CNN), Fully Convolutional Networks (FCN) and Recurrent NeuralNetworks (RCN), probabilistic models such as Bayesian Networks andGraphical Models, and/or discriminative models such as Decision Forestsand maximum margin methods, or the like.

Health Conditions

Diabetes mellitus (commonly referred to as diabetes) may be a chronic,lifelong metabolic disease (or condition) in which a patient's body isunable to produce any or enough insulin, or is unable to use the insulinit does produce (insulin resistance), leading to elevated levels ofglucose in the patient's blood. The three most identifiable types ofdiagnosed diabetes include: pre-diabetes, type 1 diabetes, and type 2diabetes. Pre-diabetes is a condition in which blood sugar is high, butnot high enough to be type 2 diabetes. Type 2 diabetes is a chroniccondition that affects the way the body processes blood sugar. Lastly,type 1 diabetes is a chronic condition in which the pancreas produceslittle or no insulin.

Diabetes generally is diagnosed in several ways. Diagnosing diabetes mayrequire repeated testing on multiple days to confirm the positivediagnosis of a types of diabetes. Some health parameters that doctors orother suitable healthcare providers use when confirming a diabetesdiagnosis include glycated hemoglobin (A1C) levels in the blood, fastingplasma glucose (FPG) levels, oral glucose tolerance tests, and/or randomplasma glucose tests. Commonly, a healthcare provider is interested in apatient's A1C level to assist in the diagnosis of diabetes. Glycatedhemoglobin is a form of hemoglobin that is measured primarily toidentify the three-month average plasma glucose concentration that maybe used by doctors and/or other suitable healthcare providers includeweight, age, nutritional intake, exercise activity, cholesterol levels,triglyceride levels, obesity, tobacco use, and family history.

Once a diagnosis of a type of diabetes is confirmed by a doctor or othersuitable healthcare provider, the patient may undergo treatment tomanage their diabetes. Patients having their diabetes tracked ormonitored by a doctor or other healthcare provider may be treated by acombination of controlling their blood sugar through diet, exercise,oral medications, and/or insulin treatment. Regular screening forcomplications is also required for some patients. Depending on how longa patient has been diagnosed with diabetes, mHealth application 1 maysuggest a specific treatment plan to manage their condition(s). Oralmedications typically include pills taken by mouth to decrease theproduction of glucose by the liver and make muscle more sensitive toinsulin. In other instances, where the diabetes is more severe,additional medication may be required for treating the patient'sdiabetes, including injections. An injection of basal insulin, alsoknown as background insulin, may be used by healthcare providers to keepglucose levels at consistent levels during periods of fasting. Whenfasting, the patient's body steadily releases glucose into the blood tosupply the cells with energy. An injection of basal insulin is thereforeneeded to keep glucose levels under control, and to allow the cells totake in glucose for energy. Basal insulin is usually taken once or twicea day depending on the type of insulin. Basal insulin acts over arelatively long period of time and therefore is considered long actinginsulin or intermediate insulin. In contrast, a bolus insulin may beused to act quickly. For example, a bolus of insulin that may bespecifically taken at meal times to keep glucose levels under controlfollowing a meal. In some instances, when a doctor or healthcareprovider generates a treatment plan to manage a patient's diabetes, thedoctor creates a basal-bolus dose regimen involving, e.g., taking anumber of injections throughout the day. A basal-bolus regimen, whichmay include an injection at each meal, attempts to roughly emulate how anon-diabetic person's body delivers insulin. A basal-bolus regimen maybe applicable to people with type 1 and type 2 diabetes. In addition tothe basal-bolus regimen requiring injections of insulin, the treatmentplan may be augmented with the use of prescribed oral medications. Apatient's adherence to a treatment plan may be important in managing thedisease state of the patient. In instances where the patient has beendiagnosed with diabetes for more than six months, for example, a veryspecific treatment regimen must be followed by the patient to achievehealthy, or favorable, levels of glucose. Ultimately, weekly patterns ofthese medication types of treatments may be important in managingdiabetes. A mHealth application 1 may recommend treatment plans to helppatients manage their diabetes.

Exemplary Methods

Diabetes is a chronic condition that results in a patient unable to keepglucose within a normal or recommended target range. Such fluctuatingglucose levels (i.e., outside the normal or recommended target range)can lead to significant health complications. Developing meaningfulinsights is difficult with sporadic blood glucose monitoring (BGM),where only a handful of intermittent readings in a week may not serve abasis to understand patterns, and any underlying causes for thosepatterns (e.g., determining a rising BGM based on a meal type).

Continuous glucose monitoring (CGM) provides the possibility for densedata (e.g., data based on a collection frequency of every 5 minutes orless) to be automatically gathered through wearable sensors (e.g.,sub-cutaneous sensors) that provide a periodic glucose value (e.g., auser 8's glucose levels). CGM can improve diabetes care by providing acontinuous (e.g., every five minutes or less) or semi-continuous (e.g.,more than every five minutes) readout of glucose data to user 8 or otherentities (e.g., healthcare provider 7) so that the user 8 or otherentities can be more aware of the user 8's glucose levels at all timesof the day. Such data may allow a healthcare provider 7 to adjusttreatment plans for user 8 more optimally.

A CGM monitor may be a continuous analyte sensor system that includesany sensor configuration that provides an output signal indicative of aconcentration of an analyte. The CGM monitor may sense the concentrationof the analyte to determine, for example, glucose values, based on abodily fluid (e.g., interstitial fluid). The bodily fluid may beaccessed through a user's skin. The output signal, which may be in theform of, for example, sensor data, such as a raw data stream, filtereddata, smoothed data, and/or otherwise transformed sensor data, may besent to a receiver, which may be connected to the CGM monitor via awired or wireless connection and may be local or remote from the sensor.According to implementations, the CGM monitor may include atranscutaneous glucose sensor, a subcutaneous glucose sensor, acontinuous refillable subcutaneous glucose sensor, a continuousintravascular glucose sensor, or the like. The CGM monitor may be acompact medical system with one or more sensors that is inserted onto auser 8's abdomen and that includes a small cannula that penetrates theuser 8's skin. An adhesive patch may hold the monitor in place. Thesensor may sense glucose readings in interstitial fluid on a continuousor semi-continuous basis.

A transmitter may be connected to the sensor to allow the CGM monitor tosend the glucose readings wirelessly to a monitoring device. Themonitoring device may be a CGM monitor specific monitoring device, maybe a third party device, an electronic device 19, or any otherapplicable device. The monitoring device may be a dedicated monitoringdevice or an electronic device 19 that provides one or more functions inaddition to the CGM monitoring. An application or other software may beused to facilitate the analysis and/or display of the glucose readingsand associated data via the monitoring device. The monitoring device maybe used to analyze and/or view the data associated with the glucosereadings. Alternatively, or in addition, the CGM monitor may include adisplay to view glucose readings and/or associated data. The CGM monitorand/or external device may be configured to generate and/or providealerts based on the glucose data (e.g., if blood sugar levels are toohigh or too low, or showing an unfavorable trend).

By using CGM data, a time in range (TIR) value can be determined where aTIR value is based on an amount of time a user 8's glucose level iswithin a threshold band over a base time period. The threshold band maybe pre-determined, be user specific, or may be dynamically determined.

The threshold band may be a pre-determined value based on, for example,a cohort of patients. The lifestyle, habits, medical test results foreach of the patients in a cohort may be used to determine thepre-determined value. For example, one or more cohorts of patients maybe determined based on the patient's lifestyle, habits, demographics, orthe like, and a threshold band may be generated for each of the one ormore cohorts. The threshold band may be determined based on optimalresults (e.g., preferred A1C values) based on an analysis of glucoselevels over a period of time. For example, a machine learning model maybe generated using training module 310. The machine learning model maybe trained using the glucose levels of a cohort of patients as stageinputs 314 and may receive the corresponding A1C values as knownoutcomes 318. The training machine learning model may receive, asinputs, data (e.g., A1C values) of a cohort of patients and may output athreshold band (i.e., with an upper glucose limit and a lower glucoselimit) of glucose levels for that cohort of patients. Alternatively, thethreshold band may be a pre-determined value for a general populationsuch that it is not cohort specific. According to implementations, a TIRthreshold band is between approximately 70 mg/dL and approximately 180mg/dL. A TIR value may be the amount of time that user 8's glucose levelis within the TIR threshold band for a base period of time. According toimplementations of the disclosed subject matter, the base period of timemay be 24 hours though it will be understood that more granular changesin TIR values may be determined based on reducing the base period oftime to be less than 24 hours and broader changes may be determinedbased on increasing the base period of time to be greater than 24 hours.

A user-specific threshold band may be determined based on attributesabout a user 8. The attributes may be medical history, physical history,demographics, or the like. According to an implementation, theuser-specific threshold may be generated using a machine learning modeltrained using training module 310. The machine learning model mayreceive updated attributes based on user 8 and, may re-train itself viausing the updated attributes through the comparison results 316component. As an example, a change in user 8's weight may be a change inattribute that is provided to the comparison results 316 component suchthat the machine learning model updates a previously provided thresholdband based on the updated weight. Accordingly, a user-specific thresholdband may change from time to time, based on one or more attributes ofthe user 8. Similarly, a dynamically determined threshold band may bedetermined based on changes in one or more attributes related to theuser 8, a cohort of users, external conditions, environmentalconditions, updated recommendations, or the like.

As applied herein, a user vector (e.g., patient vector) may be anybehavior, activity, good (e.g., consumable good), service, parameter, orvalue that is or can be associated with a given patient and that can bechanged. A patient vector may be changed to improve a TIR state or a GVstate of a user 8, as further disclosed herein. As examples, a patientvector may include one or more of medications, food consumptionproperties, exercise values, psycho-social parameters,social-determinant parameters, or the like.

As applied herein, a user attribute (e.g., patient attribute) may be anattribute or characteristic associate with a patient. As compared to apatient vector, a patient attribute may be one that cannot be easilymodified or changed. As examples, patient attributes may include asocial attribute, medical history or condition, patient preference,metabolic attribute, patient demographic, or the like.

FIG. 4A shows an example CGM based glucose level trace 402 for a user 8.The time period shown via FIG. 4A may be a full day (i.e., 24 hourperiod). As shown, the user 8's glucose level may have a TIR by beingwithin a threshold range represented by an upper threshold 404A and alower threshold 404B for a portion of the day except for during TARduration 402A and a TBR duration 402B. User 8 may be provided such agraphical display during the day or after the completion of the day.Accordingly, the CGM data may be provided to user 8 and inform user 8 ofher current glucose levels and/or trends associated with her currentglucose levels.

FIG. 4B shows an example CGM based report 406 which may be provided touser 8 or a healthcare provider 7. The report may be in an AmbulatoryGlucose Profile (AGP) format and may include a number of metrics (e.g.,10 metrics) as well as graphical data. The report may include glucosestatistics and targets 408, an AGP profile 410, daily glucose profiles412, time ranges 414, and the like. However, most patients with diabetesmay not be able to interpret such CGM data and/or AGP information toaffect change in glucose levels. Similarly, healthcare providers 7 mayrequire multiple patient consultations to interpret the data providedvia CGM monitoring and/or AGP information to even temporarily optimizeglucose levels. Techniques disclosed herein provide tracking ofessential parameters to manage user 8's health.

According to implementations disclosed herein, the CGM data may be usedto recommend changes based on one or more patient vectors, as furtherdisclosed herein. A CGM event (e.g., a change in CGM state, a portion ofa CGM trace, etc.) may be defined as a discernable region of a CGMtracing that is correlated to a diabetes self-management activity(DSMA). A CGM trace may be used to identify a CGM trend or may be a CGMtrend, as further applied herein. A DSMA may be a change in or additionof a medication, a change in or addition of a food, a change in oraddition of an exercise, or the like. The CGM may drive automatedcoaching to a user 8. Similarly, the CGM based outcome (e.g., an outcomein glucose properties based on the automated coaching and/or DSMA) maydrive coaching for future DSMA and/or provide tailored and specificdecision-support for healthcare providers 7.

According to implementations, a detect, inform, classify, and engage(DICE) framework may outline techniques to detect various diabetesrelated events from a CGM trace, inform a healthcare provider 7 and/oruser 8 about the progress along an optimized pathway via one or morevisualizations, classify a detected event into one or more classesand/or 2D CGM quadrant starting states for additional intervention,and/or engage and coach patients towards improved outcomes. Thetechniques associated with the DICE framework synthesize data frommultiple domains such as metabolic data, lifestyle data, socioeconomicdata, clinical data, and the like to enhance patient care. The automatedCGM event detection and classifications techniques disclosed hereinallow enhanced quality of care by increasing accuracy and reducingerrors. Automated coaching based on various quantitative methodologiesallows scalability and increased reach of every patient in need of careand/or support. The visualizations provided herein reduce the databurden on a user 8 and/or healthcare provider 7 by distilling dense CGMdata and other applicable data into easy to consume charts, graphs,and/or other visualizations. FIG. 5A shows a method 500 for providingoptimized pathways for improving the glucose state of a user 8. At 502,a user 8's glucose levels may be received. The glucose levels may beprovided on a continuous or semi-continuous basis by a CGM monitor, asdisclosed herein. The glucose levels may be received at a component ofthe CGM monitor itself or may be received at a local or remote componentsuch as an electronic device 19, mHealth application 1, one or moreservers 29, or the like. The glucose levels may be providedautomatically from the CGM monitor to one or more components, may bepushed upon collection of glucose levels, or the CGM monitor may bepinged to transmit one or more collected glucose levels.

As an example, a user 8 may attach a CGM monitor to her body and the CGMmonitor may collect glucose level readings every five minutes. The CGMmonitor may be connected to the user 8's mobile device (e.g., via anetwork connection, local area network connection, wide area networkconnection, WiFi connection, Bluetooth® connection, etc.). According toa first example implementation, the CGM monitor may automaticallytransmit a glucose level reading to user 8's mobile device each time areading is collected (e.g., every 5 minutes). Alternatively, or inaddition, the CGM monitor may store one or more glucose level readingssuch that they are sent to the user 8's mobile device as a group ofmultiple readings and/or when the user 8's mobile device or anothercomponent requests that the one or more glucose level readings aretransmitted.

At 504 of FIG. 5A, time in range (TIR) values associated with theglucose level readings are determined. In range glucose values maycorrespond to the amount of time glucose level readings are within agiven range, ratio of glucose level readings within range to out ofrange, count of glucose level readings in range to out of range or thelike. The TIR values may distinguish the user 8's glucose levels fromthe times when they are within the range to the times when they areoutside of the range. As shown in FIG. 4A, the glucose levels may beconsidered in range when within an upper threshold 404A and a lowerthreshold 404B. The upper threshold 404A may be 180 mg/dL and the lowerthreshold 404B may be 70 mg/dL such that a TIR value for a given patientmay correspond to the amount of time that the patient's glucose levelsare between 70 mg/dL and 180 mg/dL.

The TIR value determined at 504 of FIG. 5A may be based on an amount oftime user 8's glucose level is within a threshold band over a baseperiod of time. The base period of time may be a single 24 hour day ormay be a different base period. The base period may be pre-determined(e.g., by user 8, by a healthcare provider 7, pre-programmed, etc.), ormay be dynamically determined based on one or more factors. The one ormore factors may be patient vectors, patient attributes, a current orprevious TIR state, or the like.

According to an implementation, the TIR value may be for the base periodor may be a TIR value associated with the patient over a number of baseperiods. For example, a TIR value for user 8 may be determined for eachday for a total of ten days. The TIR value from each of the 10 days maybe combined using any applicable technique (e.g., an average) such thatthe TIR associated with the user 8 over the ten days is the combined TIRvalue.

According to an implementation, the TIR value may be filtered such thatanomalies in glucose levels are removed or weighted less then glucoselevel readings that are not flagged as anomalies. As an example, aglucose level reading of 65 mg/dL during a first reading may increase to200 mg/dL in the very next second reading five minutes after the firstreading. A third reading five minutes after the second reading mayindicate a glucose level of 68 mg/dL. A filter such as one using adensity-based techniques (e.g., k-nearest neighbor, local outlierfactor, isolation forests, etc.), one using subspace, correlation-based,and/or tensor-based outlier detection for high-dimensional data, oneusing one-class support vector machines, one using replicator neuralnetworks, autoencoders, variational autoencoders, long short-term memoryneural networks, one using Bayesian networks, one using Hidden Markovmodels (HMMs), one using cluster analysis-based outlier detection, oneusing deviations from association rules and frequent item sets, oneusing fuzzy logic-based outlier detection, one using ensembletechniques, using feature bagging, score normalization and differentsources of diversity, one using convolutional LSTM with mixtures ofprobabilistic principal component analyzers, and/or the like may be usedto identify anomalies and/or glucose level reading that may be read inerror, may be insignificant outliers, or the like. One or more of suchfiltering techniques may also be using with machine learning modelsdisclosed herein. According to this implementation, a TIR valueassociated with user 8 may be in view of the glucose level readingsbeing filtered through such one or more filters. Such filtering mayprevent providing optimized pathways, as further disclosed, that aretainted due to anomalies, outlier data, and/or irregular readings.

At 506 of FIG. 5A, a TIR state for the user 8 may be determined based onthe one or more TIR values associated with the user 8. The TIR state maybe a state associated with the TIR value alone or may be based on one ormore other factors (e.g., frequency of glucose readings, quality ofglucose readings, another sensed reading, a patient-based factor, etc.).For simplicity, this disclosure will discuss a TIR binary state based onTIR values alone (i.e., a good TIR state and a bad TIR state). However,it will be understood that the TIR state may be a multi-dimensionalstate based on the TIR value and one or more other factors. As appliedherein a good TIR state (e.g., a first TIR state) corresponds to a TIRratio greater than a TIR cutoff and a bad TIR state (e.g., a second TIRstate) corresponds to a TIR ratio less than the TIR cutoff.

FIG. 6A shows a chart 600 of TIR states for a plurality of differentpatients. The chart includes four quadrants based on a TIR ratio and aGV ratio, as further disclosed herein. The TIR state is based on the TIRaxis which corresponds to the Y access in the chart 600. The TIR ratiois the percentage of time over the base time period that the glucoselevels of a patient are within the threshold band. Alternatively, theTIR ratio may be the percentage of time over the base time period thatthe glucose levels of a patient are within the threshold band formultiple base time periods, such that the TIR ratio is a computed (e.g.,averaged) value over the multiple base time periods.

A TIR ratio value may be designated as a cutoff for a good TIR stateversus a bad TIR state. Chart 600 of FIG. 6A includes a cutoff of 0.5such that a TIR ratio above 0.5 is considered a good TIR state (e.g.,where a user 8's glucose level is within a threshold band for over 50%of the time or calculated readings) and a TIR ratio below 0.5 isconsidered a bad TIR state (e.g., where a user 8's glucose level isoutside the threshold band for over 50% of the time or calculatedreadings). The cutoff may be pre-determined or dynamically determined. Apre-determined cutoff may be based on a medical standard or may bedesignated by a healthcare provider 7 for a cohort or a user 8. Adynamically determined cutoff may be based on a cohort or a given user 8and may be determined by a machine learning model. The machine learningmodel may receive, as inputs, patient vectors, patient attributes, pastpatient TIR or GV values or changes, or the like and may output a cutoffspecifically for a user 8 or cohort that the inputs are associated with.Accordingly, the cutoff may be tailored to a value that is consideredoptimal for the corresponding user 8 or cohort that the input data wasbased on.

As shown in chart 600, patients with a TIR value above the cutoff of 0.5are considered to have good TIR state and patients with a TIR valuebelow the cutoff of 0.5 are considered to be in a bad TIR state. It willbe understood that if the cutoff was shifted, the number of patientswith good or bad TIR states would change accordingly. For example, ifthe TIR ratio was adjusted to 0.9 instead of 0.5, most patients would bein a bad TIR state.

At 508, of FIG. 5A, glucose variability (GV) values associated with theglucose level readings for a given user 8 are determined. Glucosevariability values may measure the amount of change in glucose over atime period to utilize the fluctuations in glucose values to improvediabetes management. A GV value may be a standard deviation (SD) value,a coefficient of variance (CV) or any other applicable fluctuationmeasurement value.

The SD may be a measure of the amount of variation or dispersion of aset of glucose values (e.g., collected over an hour, over a day, or anyother applicable period of time). A low SD may indicate that the glucosevalues tend to be close to a mean of the set of glucose values. A highSD may indicate that the values are spread out over a wider range. TheSD of glucose values may be the square root of the variance of theglucose values. The SD of glucose values may be calculated as shown inEquation 1:

$\begin{matrix}{\sigma = \sqrt{\frac{\Sigma{{x - \mu}}^{2}}{N}}} & ( {{Equation}\mspace{14mu} 1} )\end{matrix}$Where x is each of a glucose value in a set of glucose values associatedwith the patient, μ is the mean of the glucose values in the set ofglucose values associated with the patient, and N is the number of datapoints in the set of glucose values associated with the patient.

A CV may be a standardized measure of dispersion of a probabilitydistribution or frequency distribution. The CV for a patient's glucoselevels may be calculated by determining the ratio of the standarddeviation of the glucose levels to the mean of the glucose levels. TheCV may shows the extent of variability in relation to the mean of theglucose levels over a period of time. The CV may be calculated as shownin Equation 2:

$\begin{matrix}{{Cv} = \frac{\sigma}{\mu}} & ( {{Equation}\mspace{14mu} 2} )\end{matrix}$

As stated, the GV value may be a SD value or a CV value. According to animplementation, the type of GV value (e.g., SD value, CV value, etc.)may be based on a user 8 or may be based on current or historical patentvectors, patient attributes, or other information related to user 8.According to another implementation, the type of GV value may bedetermined by a healthcare provider 7 or by a machine learning modelconfigured to output the optimal type of GV value based on one or moreinputs such as patient vectors, patient attributes, historical analysis,or the like.

At 510 of FIG. 5A, a GV state for the user 8 may be determined based onthe one or more GV values associated with the user 8. The GV state maybe a state associated with the GV value alone or may be based on one ormore other factors (e.g., frequency of glucose readings, quality ofglucose readings, another sensed reading, a patient-based factor, etc.).For simplicity, this disclosure will discuss a GV binary state based onGV values alone (i.e., a good GV state and a bad GV state). However, itwill be understood that the GV state may be a multi-dimensional statebased on the GV value and one or more other factors. As applied herein agood GV state (e.g., a first GV state) corresponds to a GV value greaterthan a GV cutoff and a bad GV state (e.g., a second GV state)corresponds to a GV value less than the GV cutoff.

FIG. 6A shows a chart 600 of GV states for a plurality of differentpatients. The chart includes four quadrants based on a TIR ratio and aGV, as disclosed herein. The GV state is based on the GV axis whichcorresponds to the X access in the chart 600. The GV may be the SD or CVassociated with the glucose level of a patient over a period of time.Alternatively, the GV may be the SD or CV associated with the glucoselevel of a patient over multiple periods of time, such that the GV is acomputed (e.g., averaged) value over the multiple time periods.

A GV value may be designated as a cutoff for a good GV state versus abad GV state. Chart 600 of FIG. 6A includes a cutoff of 0.8 such that aGV value above 0.8 is considered a good GV state and a GV value below0.8 is considered a bad GV state. The cutoff may be pre-determined ordynamically determined. A pre-determined cutoff may be based on amedical standard or may be designated by a healthcare provider 7 for acohort or a user 8. A dynamically determined cutoff may be based on acohort or a given user 8 and may be determined by a machine learningmodel. The machine learning model may receive, as inputs, patientvectors, patient attributes, past patient TIR or GV values or changes,or the like and may output a cutoff specifically for a user 8 or cohortthat the inputs are associated with. Accordingly, the cutoff may betailored to a value that is considered optimal for the correspondinguser 8 or cohort that the input data was based on.

As shown in chart 600, patients with a GV value above the cutoff of 0.8are considered to have good GV state and patients with a GV value belowthe cutoff of 0.8 are considered to be in a bad GV state. It will beunderstood that if the cutoff was shifted, the number of patients withgood or bad GV states would change accordingly. For example, if the GVvalue was adjusted to 0.9 instead of 0.8, more patients would be in abad GV state than when compared to when the cutoff is 0.8. According toan implementation, an optimal cutoff value for distinguishing between agood state and a bad state may be 0.7.

As shown in FIG. 6A, four quadrants are created based on the Y axis (TIRratios) and X axis (GV values) segregated based on a TIR cutoff value(i.e. 0.5 in the example shown in FIG. 6A) and GV cutoff value (i.e.,0.8 in the example shown in FIG. 6A). Patients in the top left quadrant602 correspond to those within a good TIR state (i.e., above a TIRcutoff) and a bad GV state (i.e., lower than a cutoff GV). This statemay be considered a Good-Bad (G-B) state where the firstcharacterization (i.e., Good) corresponds to a TIR state and the secondcharacterization (i.e., Bad) corresponds to a GV state. Patients in thebottom left quadrant 604 correspond to those within a bad TIR state(i.e., below a TIR cutoff) and a bad GV state (i.e., lower than a cutoffGV). This state may be considered a Bad-Bad (B-B) state. Patients in thebottom right quadrant 606 correspond to those within a bad TIR state(i.e., below a TIR cutoff) and a good GV state (i.e., higher than acutoff GV). This state may be considered a Bad-Good (B-G) state.Patients in the bottom left quadrant 604 correspond to those within abad TIR state (i.e., below a TIR cutoff) and a bad GV state (i.e., lowerthan a cutoff GV). This state may be considered a Bad-Bad (B-B) state.Patients in each of the quadrants 602, 604, and 606 may be consideredpatients having non-ideal states such that at least one of the TIR stateor the GV state is a non-optimal state (e.g., a “bad” state). Patientsin the top right quadrant 606 correspond to those within a good TIRstate (i.e., above a TIR cutoff) and a good GV state (i.e., higher thana cutoff GV). This state may be considered a Good-Good (G-G) state.Patients in quadrant 608 may be considered patients in an ideal statesuch that both of the TIR state and the GV state is an optimal state(e.g., a “good” state).

As shown at 512 of FIG. 5A, the starting state for a given patient maybe based on the patient's TIR state and GV state. The starting state fora user 8 may correspond to the quadrant that the user 8's TIR state andGV state falls into, as shown in FIG. 6A. For example, a user 610, asshown in FIG. 6A, may have a TIR ratio that does not meet the TIR cutoffand, thus, is in a Bad TIR state and a GV that is higher than the GVcutoff and, thus, is in a Good GV state. Accordingly, the user 610 maybe in a non-ideal starting Bad-Good state, represented by the bottomright quadrant 606 in the example shown in FIG. 6A, as determined at 514of FIG. 5A. The non-ideal starting Bad-Good overall state of user 610may be the user 610's state at a point in time and may change over time,as further disclosed herein.

A non-ideal starting state, as determined at 514 of FIG. 5A may indicatethat a user 8's diabetes management is not optimal. For example, anon-ideal starting state may indicate a low TIR and/or a non-optimal GV.Accordingly, a non-ideal starting state may require an adjustment to thecorresponding user 8's diabetes management such that the user 8's statecan change from the non-ideal state to an ideal-state.

According to an implementation, the two dimensional framework describedherein and as shown in FIG. 6A may be implemented into a productionsystem via a novel data integration Extract, Transform, Load (ETL)process. The process may extract the CGM data obtained by a CGM monitorand analyzed by either the CGM monitor, an electronic device 19, and/orany other applicable component. The extracted data may be transformedand/or loaded into a production database that may include one or moremachine learning models and may determine a starting state (e.g., at 512of FIG. 5A).

Accordingly, in a macro view of the state based data of a user 8 (e.g.,a starting state) can be represented by two orthogonal parameters, theTIR state and the GV state. As disclosed herein, the corresponding statemay be visualized and reported to the user 8, healthcare provider 7, orthe like, to assess an overall glucose health status (e.g., as shown inFIG. 6A). The state based data may be used to provide overall glucosehealth recommendations (e.g., via an optimized pathway, as furtherdisclosed herein).

At 516 of FIG. 5A, an optimized pathway to reach an ideal state may begenerated. The optimized pathway may be one or more adjustments to oneor more patient vectors and may be determined based on the non-idealstate (i.e., Good-Bad, Bad-Bad, or Bad-Good states), patient vectors,and/or patient attributes. The optimized pathway may be adjustments toone or more patient vectors including, but not limited to, medications,food consumption properties, exercise values, psycho-social parameters,and/or social-determinant parameters.

An adjustment to medication may be provided based on a user 8's currentmedications or may be based on new medications that the user 8 may beprovided. The adjustment may be made by adjusting a dose of a medicine,by adding or removing a medicine, by changing the time or frequency amedicine is consumed, by changing the environment (e.g., the type offood consumed with the medication) associated with the medication, orthe like. For example, consumption of a specific medication that user 8is currently consuming may be adjusted to a higher dose.

An adjustment to food consumption properties may including changing,removing, adding, or otherwise modifying one or more foods, food groups,food types, food consumption times, food pairings, food and medicationpairings, or the like. For example, based on a patient attributeindicating that the glucose level of a patient increases beyond thethreshold band after consuming food, the patient may be provided analert to consume food during times when a current glucose level is low.

An adjustment to exercise values may include changing, removing, adding,or otherwise modifying one or more exercises, exercise types, exercisedurations, exercise times, or the like. For example, the GV for a givenpatient may be more stable if the patient exercise earlier in the dayand, thus, an adjustment may be made to prioritize exercising in themorning.

Psycho-social parameters and/or social-determinant parameters may alsobe adjusted or modified and may include changing, removing, adding, orotherwise modifying meditation schedules or types, social activities,interactions, and/or durations or frequencies of the same.

An optimized pathway may be generated at 516 using a machine learningmodel. The machine learning model may be trained as shown in FIG. 3B.The machine learning model may receive, as inputs, one or more ofpatient vectors, the starting state (e.g., TIR state, GV state, Good-Badstate, Bad-Bad state, Bad-Good state, etc.), patient attributes, and CGMproperties. The machine learning model may produce an output of anoptimized pathway based on such inputs. The optimized pathway may be anadjustment to one or multiple patient vectors, as disclosed herein.

At 518 of FIG. 5A, the optimized pathway may be provided to the patientdirectly (e.g., to a user 8 via mHealth application 1, using anelectronic device 19, etc.), may be provided a healthcare provider 7, orto both. The optimized pathway may be an outline of changes to one ormore patient vectors, may be an automatic adjustment to one or morepatient vectors, or may be provided incrementally based on one or moreactions, timings, levels, values, or the like. An incrementally providedoptimized pathway may be provided based on the corresponding one or morepatient vectors to cause change to the one or more patient vectors. Asan example, if a change to a patient vector includes consuming food whenthe patient's glucose level is at a lower end of the threshold band, amobile device alert may be provided when the patient's CGM monitorrecords such a glucose level. The mobile device alert may provide anindication to the user that the user should consume food within a giventime period based on the alert.

An optimized pathway may also be provided on a periodic basis (e.g.,daily, hourly, weekly, etc.) or based on triggers, where thepre-determined times are based on the changes based on the optimizedpathway. For example, an optimized pathway that makes modifications to apatient's eating schedule may be provided using alerts during mealtimes. As another example, an optimized pathway that makes modificationsto a patient's medication may be provided using alerts during medicationdelivery times.

The frequency, manner, and/or mode of providing an optimized pathway maybe based on the primary actions or variables associated with successfulimplementation of the optimized pathway. A habit index may be determinedfor a patient or a cohort of patients with one or more like attributes.The habit index may be a categorization of the patient's behavior andmay be a habit designation (e.g., frequent communication, in-frequentcommunication, technological communication, telephonic communication,human communication, graphic communication, time of day communication,etc.), may be a value or score, or may be any other applicabledesignation that provides an indication of a patient's behavior toproperly tailor providing an optimized pathway.

A habit index may be determined based on habit or preferences includingfrequency-based factors, time-cue based factors, context-cue basedfactors, and/or the like. The habit index may be used to provide apatient's optimized pathway to the patient such that the optimizedpathway may be provided in accordance with the habit index. As anexample, a habit index may indicate that a user 8 prefers minimalcommunication and prefers any communication to be conducted via mHealthapplication. Accordingly, the patient vector changes via an optimizedpathway may be provided to user 8 via the mHealth application once aday. Accordingly, a habit index may be used to provide an optimizedpathway to a patient in a personalized manner based on the patientsindividual behavior preferences.

FIG. 5B shows an example implementation flowchart 540 based on CGM. Step512 of FIG. 5B corresponds to step 512 of FIG. 5A and includesdetermining a starting state for a given patient based on the patient'sTIR state and GV state, as disclosed herein. At 520, a determinationregarding whether the starting state determined at 512 is an idealstate. If the starting state is an ideal state, at 522, a CGM monitormay continue to perform CGM. If the starting state is not an ideal state(i.e., a non-ideal state such as a Good-Bad, Bad-Bad, or Bad-Goodstate), then, at 524, one or more non-ideal state attributes may bedetermined. The non-ideal state attributes may be the values of TIR orGV, changes in TIR or GV, or the like. At 526, patient vectorsassociated with the patient may be identified. The patient vectors maybe provided by a user 8, by a healthcare provider 7, obtained viaelectronic device 19, via servers 29, or any other applicable means.

At 528, an optimized pathway to transition the patient from thenon-ideal state to an ideal state may be generated. It will beunderstood that a reaching an intermediate non-ideal state may be partof reaching an ideal state. For example, a patient with a startingnon-ideal state of Bad-Bad (i.e., a bad TIR state and a bad GV state)may be provided an optimized pathway that first transitions the patientto a Good-Bad or a Bad-Good state before reaching a Good-Good state. Amachine learning model may output the optimized pathway including one ormore patient vector changes based on inputs that include one or more ofa TIR state or value, GV state or value, one or more patient vectors,one or more patient properties, a CGM event, and/or the like. At 530,the optimized pathway may be provided to the patient. The optimizedpathway may be provided based on a habit index associated with thepatient to increase the probability that the patient follows theoptimized pathway. In addition to providing the optimized pathway at 530and/or after providing the optimized pathway at 530, the CGM monitor maycontinue CGM at 522 and the flowchart 540 may iteratively repeat itselfby starting at 512 based on continuing CGM at 522. The flowchart 540 mayoccur at any applicable time period that is predetermined or that isdynamically determined for a given patient or a cohort of patients.

FIG. 6B shows a chart 612 and chart 614. The first chart 612 showsmultiple states for a given patient over the course of a number ofmonths. For example, an initial state of the patient shown by 613A is aGood-Good state (i.e., Good TIR state and Good GV state) and thesubsequent state after the initial state, shown by 613B, is a Bad-Goodstate (i.e., a Bad TIR state and a Good GV state). Chart 612 shows thevarious states for the patient over the course of the months. Each state(e.g., 613A, 613B, etc.) may be a representative state for that periodof time. For example, the initial state shown by 613A may be the averageof all states during the first month or may be the state for a given daysuch that the same day of the month is used for each of the months shownin chart 612. Chart 614 of FIG. 6B shows the same states as chart 612.However, chart 614 shows the two-dimensional state-based quadrants thatenable a viewer to see the distribution of states as they relate tocorresponding TIR states and GV states. Chart 612 and/or chart 614 maybe provided to a user 8 or a healthcare provider 7 to enable a viewer tobetter understand the status of the user 8's state statuses.

FIG. 6C shows chart 616, 618, and 620, each with a varying amount ofdata. Chart 616 includes the most data with 15 months of CGM based stateinformation. Chart 618 shows 8 months of CGM based state information,and chart 620 shows 3 months of CGM based state information. A greaternumber of data points may allow a viewer to understand a patient'sglucose level based history more holistically then less data points.

FIG. 6D shows chart 621A and 621B each showing GV values for a givenpatient over fifteen months. Chart 621A shows the standard deviation(SD) of the glucose level readings whereas chart 621B shows thecoefficient of variance (CV) of the glucose level readings. As shown,the type of GV (e.g., SD, CV, etc.) applied may change GV state at agiven time. For example, 621C of chart 621A shows a SD based GV valuefor the ninth reading. As shown, 621C corresponds to a Bad GV state.However, the same corresponding ninth reading's CV based GV value,represented by 621D in chart 621B corresponds to a Good GV state. Thetype of GV (e.g., SD, CV, etc.) to be applied may be selected based onone or more factors such as, but not limited to, patient vectors,historical glucose information, patient properties, or the like. It willbe understood that the Respective Event numbers in FIGS. 6B-6D (i.e.,Respective Events 1-16 in FIGS. 6B and 6D, Respective Events 1-16 in 616of 6C, Respective Events 1-9 in 618 of 6C, and Respective Events 1-4 in620 of FIG. 6C) are chronological indications relative to each other andnot reference numbers.

FIG. 6E shows a chart 622 of the various states of each of a pluralityof patients represented by anonymized patient IDs. For example, patient624 (i.e., patient ID 42799) may have a number of missing states (e.g.,due to missing CGM data) represented by bar 626A, a number of Good-Good(i.e., a Good TIR state and a Good GV state) states represented by bar626B, a number of Bad-Good states represented by bar 626C, and a numberof Bad-Bad states represented by bar 626D. A healthcare institution or ahealthcare provider 7 monitoring a given cohort of patients may beprovided chart 622 on a periodic basis. By reviewing visual changes inthe states shown in chart 622, a viewer may be able to easily determinea trend of state changes for all or a subset of users implementing thetechniques disclosed herein.

A healthcare institution or a healthcare provider 7 may also be providedchart 628 and/or diagram 630 of FIG. 6F. The healthcare institution or ahealthcare provider 7 may use chart 628 to review the trends in changeof statuses for a patient population. For example, a viewer providedchart 628 may be able to determine that there are more counts of patientstatuses changing from Good-Good to Bad-Good (i.e., 11) then there arein the opposite direction (i.e., 9). Such data may be used to updatedmachine learning algorithms (e.g., to improve network layers, weights,etc. to provide improved optimized pathways), to improve how optimizedpathways are provided/implemented (e.g., based on changes made to habitindexes, etc.), or the like.

Similarly, diagram 630 may be utilized by a healthcare institution or ahealthcare provider 7 to review the trends in change of statuses for apatient population. By using diagram 630, a viewer may quickly seetrends in status changes and may compare such trends over multipleperiods of time. For example, a viewer provided with diagram 630 mayeasily compare the number of status changes that changed from Bad-Goodto Good-Good (i.e., 20) and compare that to a previous month's changes.Although chart 628 and diagram 630 are shown with a number of statuschanges, it will be understood that the status changes may berepresented in any applicable manner such as using a percentage ofchange.

As disclosed herein, the optimized pathway generated at 516 of FIG. 5Amay be generated based on one or more patient vectors. FIG. 6G showschart 632 of a patient's glucose level readings 634 (e.g., as collectedusing a CGM monitor) over the course of a day. A filter or othersmoothing mechanism may be used to generate the corresponding trend line636. Chart 638 shows a first derivative 640 of the chart whichrepresents the rate of change of the glucose level readings 634 or thesmoothed trend line 636 of chart 632. Both charts 632 and 638 includepatient vectors including an exercise vector 642, a food vector 644, amedication vector 646, and another food vector 648 such that a machinelearning model may receive such vectors and their associated attributes(e.g., time of each given vector, properties of the vector such asduration of exercise, type of food, medication type and/or dosage,etc.). A patient's glucose level readings 634 may be used as inputs tothe machine learning model along with the first derivative 640 of thepatient's glucose level readings 634 or either the a patient's glucoselevel readings 634 or the first derivative 640 may be used individually.Accordingly, an output optimized pathway provided by the machinelearning model may be based on a patient's glucose level readings 634,patient vectors (e.g., exercise vector 642, a food vector 644, amedication vector 646, and another food vector 648), and/or the firstderivative 640.

As shown in FIG. 4B, an AGP report and may include a number of metrics(e.g., 10 metrics) as well as graphical data. These metric may be arenumerous and difficult to understand, both for patients and healthcareproviders 7. Techniques disclosed herein are, in part, based onminimizing the number of metrics as components of one metric of the AGPreport are determined by other measures such that not all 10 measuresare necessary since they don't give unique and independent information.FIG. 7A and FIG. 7B show that techniques disclosed herein may beimplemented using the mean glucose 704, Time Above Range (TAR) and TIR706, the variation in glucose 710, the time below range (TBR), thepercent of CGM activating time 722, and/or the variation in the standarddeviation and principal component value (PCV) of glucose 724. The meanglucose 704 may be based on the average glucose and a Glucose ManagementIndicator (GMI), where the GMI is a predicted indication of a glucoselevel. The TAR and TIR 706 may be based on a very high TAR indication(TAR_VH), a high TAR indication (TAR_H), and/or a TIR indication. Thevariation in glucose 710 may be based on the standard deviation ofglucose and the PCV. The TBR 712 may be based on the low TBR (TBR_L),very low TBR (TBR_VL), and TBR indications.

According to an implementation of the disclosed subject matter, one ormore CGM events may be classified based on the patient's glucose levels.The classifying may be based at least on a severity score associatedwith each of the one or more CGM events and/or based on one moreproperties of a curve associated with the glucose levels of a patient.FIGS. 8A, 8B, and 8C show example classifications of CGM events. Theoptimized pathway generated at 516 of FIG. 5A may be based, in part,based on the one or more classified CGM events. For example, theseverity or other property of a CGM event may be provided to a machinelearning model and the optimized pathway may be output based, at leastin part, on the one or more classified CGM events. As a an example, theseverity score may indicate the presence of sharp peaks or the frequencyof a high severity score may indicate a high amount of volatility in apatient's glucose levels. Such CGM based event information may behelpful especially if, for example, the patient has a high TIR as theTIR would not indicate an unhealthy amount of fluctuation in thepatient's glucose levels.

Applying CGM events to determine an optimized pathway may includedetection of events from a CGM trace (e.g., a series of glucose valuereadings), and classifying the events into one or more classes. Theclassification may include severity score based classifications and/orglucose categories. Severity scores may be determined using the time andshape characteristics of a CGM trace.

The severity score and/or CGM events may be determined for individualfluctuations in CGM data and may be part of a micro view of the CGM. Theseverity score and/or CGM events may be used for real-time coaching orbehavior outputs (e.g., in the moment coaching regarding medications,diet, exercise, etc.). Accordingly, techniques disclosed herein provideboth a macro view of the CGM data (e.g., using state data as describedin FIG. 5A) and a micro view of the CGM data (e.g., using the severityscores and/or CGM events) to provide both real-time and overall healthimprovement feedback.

FIG. 8A shows a chart 800 with a CGM trace 802 including a CGM event802A. FIG. 8B shows a chart 804 with a CGM trace 806 including a CGMevent 806A. The CGM event 802A may be detected based on one or moremathematical methods. In the example provided in FIG. 8A, the CGM event802A may be classified based on the clinical significancemulti-parameter CGM classification such as three parameters: glucose atthe beginning, severity, glucose at the end (b, s, d).

The parameter b may correspond to the glucose category at or near thebeginning of a given CGM event. The glucose category b may be a scalesuch as a very high (e.g., +2), high (e.g., +1), in range (e.g., 0), low(e.g., −1), or very low (e.g., −2). In the example of CGM event 802A, bcorresponds to 0 as the glucose level indicated by trace 802 is withinthe threshold range 803 at the beginning of the CGM event 802A, as shownvia the trace 802 being within the threshold range indicated by 803 atthe start of the CGM trace 802 when the trace 802 curves up towards thepeak of the CGM event 802A. In the example of CGM event 806A of FIG. 8B,b corresponds to 0 as the glucose level indicated by trace 806 is withinthe threshold range 805 at the beginning of the CGM event 806A, as shownvia the trace 806 being within the threshold range 805 at the start ofthe CGM event 806A when the trace 806 curves up towards the peak of theCGM event 806A.

The parameters may correspond to a severity score that encompasses boththe height of the curve of a CGM event and how long the curve staysabove target. The severity score s may be expressed as a value (e.g., 0through 9) that indicates the height of the curve of a CGM event and theduration that the curve stays above target. The severity score may becalculated via any applicable technique that provides a severity scorebased on the combination of the height of a CGM curve and the durationof the corresponding trace being outside threshold range. As asimplified example, a value associated with the height of the curve maybe multiplied by a value associated with the duration of the trace beingoutside a threshold range. One or both of the height and duration valuesmay be greater than one. According to an implementation, the height andthe duration may be allocated different weights such that severity scoreis based more heavily on one of the height or the duration. A higherseverity score may indicate a higher combination of the height andduration above target. A lower severity score may indicate a lowercombination of the height and duration above target. Accordingly, alower severity score may be more desirable than a higher severity score.

In the example of CGM event 802A, the parameters corresponds to aseverity score of 6 determined based on the height of the curveassociated with CGM event 802A and the duration that the trace 802 isoutside the threshold range 803. In the example of CGM event 806A, theparameter s corresponds to a severity score of 2 determined based on theheight of the curve associated with CGM event 806A and the duration thatthe trace 806 is outside the threshold range 805. The height of thecurve and the duration of time outside a target threshold range for theCGM event 802A is greater than the height of the curve and the durationof time outside a target threshold range for the CGM event 806A, asshown in FIGS. 8A and 8B. Accordingly, the severity score for CGM event802A is higher (i.e., 6) when compared to the CGM event 806A (i.e., 2).

The parameter e may correspond to the glucose category at or near theend of a given CGM event. The glucose category b may be a scale such asa very high (e.g., +2), high (e.g., +1), in range (e.g., 0), low (e.g.,−1), or very low (e.g., −2). In the example of CGM event 802A, ecorresponds to 1 as the glucose level indicated by trace 802 is higherthan the range 803 at the end of the CGM event 802A, as shown via thetrace 802 being approximately outside the threshold range indicated by803 at the end of the CGM trace 802 when the trace 802 flattens outafter the peak of the CGM event 802A. In the example of CGM event 806Aof FIG. 8B, e corresponds to −1 as the glucose level indicated by trace806 is below the threshold range 805 at the end of the CGM event 806A,as shown via the trace 806 being within the threshold range 805 at theend of the CGM event 806A when the trace 806 flattens and changesdirection below the threshold range 805.

According to another implementation, CGM events may be characterizedusing one more other techniques. For example, CGM events may becharacterized based on a severity score and shape of the CGM event.FIGS. 8C and 8D show example CGM events characterized by a severityscore and CGM event trace shape. The severity score may be calculatedbased on the height of a CGM trace and the duration of the trace beingoutside a threshold glucose range, as disclosed herein. The shape of aCGM event may be categorized in any applicable manner such as, forexample, a wide category, a tall category, and a normal category. Suchcategories may be based on the start, peak, and ending of a given CGMevent and the parameters associated with classifying a CGM trace as agiven category may be pre-determined or may be determined based on agiven patient, a plurality of CGM traces, or the like. For example, aratio of the area outlined by a given CGM trace and the height of thetrace may be used to classify a CGM trace.

FIG. 8C shows a chart 810 with a CGM trace 812, threshold range 803, andthree CGM events 812A, 812B, and 812C. The first CGM event 812A has aseverity score of 8 and a CGM trace shape that is characterized as Wide.The second CGM event 8128 has a severity score of 5 and a CGM traceshape that is characterized as Tall. The third CGM event 812C has aseverity score of 0 and a CGM trace shape that is characterized asNormal. The severity score of the third CGM event 812C is 0 as the trace812 at the peak of the CGM event 812C is within a threshold range 813.

According to implementations, a CGM trace shape may also becharacterized as short. Additionally, a machine learning model may beused to identify a CGM trace shape based on, for example, past CGM traceshapes. The machine learning model may be updated based on updated CGMtraces. For example, updated glucose values may be calculated by a CGMmonitor after an optimized pathway is provided based on a severityscore, a CGM trace shape, or the like. The updated glucose values mayencompass the effect that the optimized pathway has on the user. Theupdated glucose values may be used to generate an updated CGM trace thatis provide to the machine learning model to update the model. Forexample, if the optimized pathway did not improve a user's condition,the machine learning model may be updated to improve its output during asubsequent or future iteration.

FIG. 8D shows a chart 820 with a CGM trace 822, threshold range 823, andtwo CGM events 822A and 822B. The first CGM event 822A has a severityscore of 9 and a CGM trace shape that is characterized as Tall. Thesecond CGM event 822B has a severity score of 8 and a CGM trace shapethat is characterized as Normal.

One or more clinically significant CGM events for a given user 8 may becategorized using CGM categorization (e.g., b, s, e of FIGS. 8A and 8B,or the severity score and shape characterization of FIGS. 8C and 8D, orany other applicable characterization). An optimized pathway (e.g., viaautomated coaching messages, DSMA, etc.) may then be sent to a user 8further based on the CGM event characterization.

FIG. 9 includes a flowchart 900 for an implementation of the disclosedsubject matter. At 902, a plurality of optimization profiles forreaching an ideal state from a non-ideal state may be generated. Theplurality of optimization profiles may not be patient specific but maybe each be generated for combinations of a plurality of patient vectorsand patient attributes. The plurality of optimization profiles may begenerated using a machine learning model trained as provided in FIG. 5Bas disclosed herein. The plurality of optimization profiles may beprovided as outputs to the machine learning model and may be based on acohort of past patients and may further be based on successful orunsuccessful attempts to reach an ideal state from a non-ideal state.

The plurality of optimization profiles may be each be associated withone or more patient attributes and/or patient vectors. For example, fora given set of patient vectors and patient attributes, a specificoptimization profile may be generated for each possible non-ideal state(e.g., Good-Bad, Bad-Bad, Bad-Good, etc.).

At 904, a TIR state for a given patient may be determined and at 906, aGV state for a given patient may be determined, in accordance withtechniques disclosed herein. At 908, one or more patient vectors and oneor more patient attributes for the given patient may be received. Thepatient vectors and/or patient attributes may be provided by the givenpatient, by a healthcare provider 7, obtained via electronic device 19,via servers 29, or any other applicable means.

At 910, an optimization profile based on the patient vectors and thepatient attributes may be identified. Optimization profile may include alimited number of optimized pathways, where each optimized pathway maycorrespond to a given combination of TIR states and GV states. Forexample, an optimization profile may include an optimized pathway for aGood-Bad starting state, a Bad-Bad starting state, and a Bad-Goodstarting state. Accordingly, a given optimization profile may beidentified based on a patient's attributes and vectors, and may includea limited number of optimization profiles based on the patient'sstarting states.

At 912, an optimized pathway may be identified from the optimizationprofile and based on the given patient's TIR state and GV state. Theoptimized pathway may be different at different for the same patienteven if all of the patient's vectors and attributes remain the same. Forexample, during a first iteration, a given patient's optimizationprofile may be identified based on the patient's attributes and vectorsat the time of the first iteration. Based on the patient's TIR state andGV state during the first iteration (e.g., a Good-Bad state), a firstoptimized pathway may be identified. However, during a second iteration,even if the given' patient's vectors are the same (i.e., such that thesame optimization profile is identified), a different optimized pathwaymay be identified based on a change in state (e.g., a Bad-Good state).At 914, the identified optimized pathway may be provided to the givenpatient and/or healthcare provider 7, in accordance with the techniquesdisclosed herein.

While steps 502-518 of FIG. 5A, 512-530 of FIG. 5B, and 902-914 of FIG.9 are depicted in a particular order, the principles of the presentdisclosure are not limited to the orders depicted therein.

FIG. 10A shows a diagram 1000 that includes a chart 1002 of CGM eventsby time and day. Such a diagram or other visual output may be providedto a healthcare provider 7 or a user 8 via an application (e.g., mHealthapplication 1) to more easily understand their CGM journey for a giventime period. Diagram 1000 includes a number of Journey days as the Yaccess and a time of day as the X axis. A viewer may receive diagram1000 and easily determine patterns on given days, times of days, and/orover a number of days or times.

FIG. 10B shows a diagram 1010 that includes a chart 1012 of total carbsconsumed by time and meal type. Such a diagram or other visual outputmay be provided to a healthcare provider 7 or a user 8 via anapplication (e.g., mHealth application 1) to more easily understandtheir dietary habits for a given time period. Diagram 1010 includes anumber of total carbs as the Y access and a time of day as the X axis. Aviewer may receive diagram 1010 and easily determine patterns on givendays, times of days, and/or over a number of days or times. For example,the user may easily see the meal types consumed over the course of a dayand the calories associated with the meal type.

FIG. 11 shows a count of severity scores for a first patient, as shownvia chart 1102 and a second patient, as shown via chart 1104. Each barin the chart 1102 and chart 1104 represents the number of times a givenseverity score was exhibited in the respective first and secondpatient's CGM data. Generally, a higher count for a lower severity scoremay be preferable as such a distribution may indicate better diabetesmanagement. A healthcare institution or healthcare provider 7 mayreceive distribution charts (e.g., chart 1102 and chart 1104) for one ormultiple patients over one or more time periods and may use thedistribution charts to monitor overall patient group progress.Alternatively, or in addition, distribution charts may be generated fora specific patient group (e.g., based on time of treatment, based onhealthcare team, patient attributes, patient vectors, etc.) and mayanalyze trends for the specific patient groups or compare trends betweenmultiple patient groups.

FIG. 12 shows a diagram 1200 of a CGM based implementation for providingcoaching to a patient based on CGM data. As shown, one or moreattributes may be provided to a CGM message generator 1212. Theattributes may include, but are not limited to, glucose values 1202,glucose trends 1204 (e.g., CGM trends, CGM event data, etc.),carbohydrate information 1206, activity information 1208, insulininformation 1210, and the like or a combination thereof. Based on theattributes, a message level may be determined. For example, the messagelevel may be an Act level 1222 where urgent action is needed (e.g., auser should take insulin or consume carbohydrates to avoid harm), anAlert level 1224 where action may be required soon, but is not urgent(e.g., a user should monitor glucose carefully), or an Advise level 1226where an information message is provided (e.g., no action needed by theuser).

The CGM message generator 1212 may be applied at 518 of FIG. 5A or 914of FIG. 9 as the CGM message generator 1212 may be used to provide anoptimized pathway based on a patient vector (e.g., the attributes1202-1210). For example, an optimized pathway may be determined based atleast in part on the attributes 1202-1210 and may be provided to acorresponding patient via the CGM message generator 1212.

FIG. 13A shows an example message 1302 provided using the CGM messagegenerator 1212. The message 1302 may be provided via a user 8'selectronic device 19. In the example provided in FIG. 13A, the messageis an Act level 1222 message and may include a required action. Asshown, the example message 1302 is, “Action Required: Hey Charlie, yourglucose is high and rising quickly. Go to the insulin computer go get aninsulin dose so that you can get back down to range.” The message 1302may be provided to the user 8 via mobile phone 1300 such that it is sentwith high importance. The high importance may result in the electronicdevice 19 providing an audible alert, a haptic alert, visual alert, orthe like, in addition to the message 1302.

FIG. 13B shows another example message 1314 provided using the CGMmessage generator 1212 via the mobile phone 1300. Message 1314 is anAlert level 1224 message and may not include an immediate action. Asshown, the example message 1314 is, “No Action Required: Charlie, yourglucose is in target but rising a bit; no action is required at thistime.” Additionally, additional information such as glucose level 1312may also be provided via mobile phone 1300 and may be provided inproximity to the related message 1314.

FIG. 13C shows another example message 1322 provided using CGM messagegenerator 1212 via the mobile phone 1300. Message 1322 is an Adviselevel 1226 message and includes general advice for a patient. As shownin FIG. 13C, the message 1322 may also include other patient vectorssuch as carbohydrate information, glucose level, insulin dose, or thelike.

Accordingly, as shown via the examples in FIGS. 13A-13C, machinelearning driven automated user coaching or CGM feedback may be providedto a user 8. Systems and methods can be used to, for example, providealerts when critical actions are necessary such as in the case ofhypoglycemia or extreme hyperglycemia. Informative messages for lesscritical glucose readings may also be provided. An insulin dosingsupport may provide correctional insulin based on a glucose trend, asone of the patient vector corrections via an optimized pathway.According to an implementation, an insulin dose may be a patient vectorthat can be adjusted based on a glucose trend (e.g., CGM event). Acurrent glucose level may also be a factor when determining an insulinadjustment amount. For example, the trend may be a key component asbolus insulin may require a time period (e.g., 30 minutes) to provide anintended result and, accordingly, a trend projection at the end of thattime period may be more useful than a current glucose level alone.

According to an implementation of the disclosed subject matter, aninsulin computer may be provided. The insulin computer may be acontextual computer that receives one or more factors as inputs toprovide behavior outputs including, for example, an amount of insulin toconsume at a given time. The insulin computer may be a part of a CGMmonitor or may be external to the CGM monitor (e.g., may be part of oneor more electronic device 19). An external insulin computer may beconnected to the CGM monitor via a wired or wireless connection such aselectronic network 32.

The insulin computer may be a software or an application that operateson the CGM monitor or an external device. For example, the insulincomputer may be part of the mHealth application 1. The insulin computermay receive one or more complex inputs and may provide behavioraloutputs. Behavior outputs may be instructions or numerical values withone or more behavior output categories including, but not limited to,whether insulin is needed, how much insulin is needed, whether glucoseis needed, how much glucose is needed, whether food consumption isneeded, how much food consumption is needed, whether exercise is needed,how much exercise is needed, or the like. The function of the insulincomputer may change based on a user's state. For example, a behavioroutput may change to safely and effectively keep the user's glucoselevel in an optimal range. In this example, a CGM trend may be used asan input to determine the optimal glucose levels.

The insulin computer may receive a CGM trend as an input. A CGM trendmay include or may be based on a CGM trace, CGM event, or the like asdisclosed herein in detail. The CGM may be based on a change in two ormore glucose readings over a period of time. The CGM trend may be basedon glucose readings provided by a CGM device. The CGM trend may changeover time such that additional glucose readings may result in a modifiedor updated trend. A past CGM trend may also be used as an input.

The insulin computer may receive dietary information as an input. Thedietary information may be provided to the insulin computer in anyapplicable manner such as by a user input, by inputting content (e.g.,an image, a video, etc.) of food prior to it being consumed or examplefood (e.g., an image of a pizza found online to represent food eaten),or the like. The content may be input using an electronic device 19 ormay be received from a resource such as an application that track's auser 8's food consumption. The dietary information may include, or theinsulin computer may calculate an insulin to carbohydrate ratio, for theuser 8 at a point in time (e.g., when the computer is used to determinea behavioral output). The insulin computer may individualize the effectsof the dietary consumption for the user 8 such that the behavior outputsbased on the dietary information for user 8 may be different for anotheruser with the same dietary information on a given day. The insulincomputer may adjust one or more behavior outputs based on the dietaryinformation and/or the insulin to carbohydrate ratio. Past dietaryinformation may also be used as an input.

The insulin computer may receive exercise (e.g., any activity)information as an input. The exercise information may be provided to theinsulin computer in any applicable manner such as by a user input (e.g.,past or planned exercise), by an exercise or health tracker (e.g., froman electronic device 19), by one or more components of the CGM monitor,one or more sensors, or the like. The exercise information may includecaloric information, heart rate information, duration of exercise,intensity of exercise, strain on body, or the like. The insulin computermay individualize the effects of the exercise for the user 8 such thatthe behavior outputs based on the exercise information for user 8 may bedifferent for another user with the same exercise information on a givenday. The insulin computer may adjust one or more behavior outputs, basedon the exercise information. Past exercise information may also be usedas an input.

The insulin computer may receive information regarding a previousinsulin dose as an input. As further discussed herein in reference toFIG. 13D, the insulin computer may determine a behavior output based onthe amount of the previous dose, the time of the previous dose in viewof one or more factors associated with user 8 (e.g., diet, exercise,individual body characteristics, CGM trend, historical data, etc.), orthe like. For example, when determining whether user 8 should consumeadditional insulin, based on the half-life of insulin consumed, theinsulin computer may determine how much insulin from a previous dose isstill in user 8's body.

The insulin computer may receive information regarding a current glucoselevel as an input. Additionally, the insulin computer may receiveinformation regarding a CGM trend (e.g., the rate of change of glucosein user 8's body) as an input. The current glucose level and/or the CGMtrend may enable the insulin computer to determine the direction of theglucose level in user 8's body (e.g., increasing, decreasing, stable,etc.) as well as the speed of change. Based on such information, theinsulin computer may adjust one or more behavior outputs. Past glucoselevels may also be used as an input.

The insulin computer may receive user 8's sensitivity to insulin as aninput. The sensitivity to insulin may be based on a pre-determined valueor may be based on historical data received at the insulin computer, CGMmonitor, or the like. According to an implementation, the sensitivitymay be adjusted overtime based on user 8's use of insulin. Accordingly,the insulin computer may update the sensitivity to insulin periodicallyor each time a user a behavior output is calculated.

The insulin computer may receive user 8's hypoglycemia history as aninput. The insulin computer may consider the time period between ahypoglycemia event and calculation of a behavior output when providing abehavior output. The insulin computer may also consider the degree ofseverity of the hypoglycemia event when providing the behavior output.As examples, if user 8's history indicates a hypoglycemia event withinthe past two days from the calculation of a behavior output or if theuser 8 experiences hypoglycemia greater than 4% for three consecutivedays, then an insulin recommendation by the insulin computer may be moreconservative than if there was no hypoglycemia event.

FIG. 13D provides an example insulin computer 1330 in accordance with animplementation of the disclosed subject matter. As shown, a time zone ofthe four time zones provided in FIG. 13D may be provided as an input tothe insulin computer 1330 such that a behavior output provided by theinsulin computer 1330 may be modified based on the time zone at the timeof providing the behavior output. Each time zone may be determined basedon the previous time that user 8 received insulin (e.g., a bolusinjection). The first zone 1334 may be a meal-time bolus from the timeof an insulin meal-time bolus administration 1332. The second zone 1336may be within two hours from the meal-time bolus administration 1332.The third zone 1338 may be from two to four hours from the meal-timebolus administration 1332. The fourth zone 1340 may be over four hoursfrom the meal-time bolus administration 1332. Each zone of the fourzones may have associated attributes as provided in FIG. 13D, includinginsulin on board (10B), correction factor (CF), insulin-to-carbohydrateratio (ICR), or the like. If within the first zone 1334, then IOB, CFadjusted based on a CGM trend, and ICR dosing may all be considered. Ifwithin the second zone 1336, then IOB and CF may not be considered butthe ICR dosing may be considered. If within the third time zone 1338then the IOB CF without a CGM adjustment, and ICR dosing may beconsidered. If within the fourth zone 1340 then IOB, CF adjusted basedon a CGM trend, and ICR dosing may all be considered.

Accordingly, based on the factors discussed herein, the insulin computermay provide a behavior output which may be, but is not limited towhether insulin is needed, how much insulin is needed, whether glucoseis needed, how much glucose is needed, whether food consumption isneeded, how much food consumption is needed, whether exercise is needed,how much exercise is needed, or the like or a combination thereof. Theinsulin computer may provide individualized contextual behavior outputssuch that a first user with inputs may receive different behavioroutputs than a second user with similar inputs, as a result of one ormore factors such as the different histories of each respective patient.

According to an implementation, one or more behavior outputs may bedetermined using a machine learning model that is part of or associatedwith the insulin computer. The machine learning model may be asupervised model trained to provide behavior outputs based known goodoutputs and/or based on past behavior outputs provided by the machinelearning model and a corresponding change in a past CGM trend afterproviding the past behavior output. For example, a machine learningmodel may be configured to provide a behavior output based on one ormore inputs, as discussed herein. The machine learning mode may receivean updated CGM trend after providing the behavior outputs. The machinelearning model may analyze the CGM trend and update the model (e.g.,update weights, a neural network, a layer, etc.) based on the CGM trendto improve future behavior outputs provided by the machine learningmodel. The machine learning model may update its model for an individual(e.g., based on behavior output provided to the user and the user's CGMtrend thereafter) or for multiple users based on feedback (i.e., CGMtrends) from one or more users.

FIG. 14 is a simplified functional block diagram of a computer that maybe configured as a host server, for example, to function as healthcareprovider decision-making server. FIG. 14 illustrates a network or hostcomputer platform 1400. It is believed that those skilled in the art arefamiliar with the structure, programming, and general operation of suchcomputer equipment and as a result, the drawings should beself-explanatory.

A platform 1400 for a server or the like, for example, may include adata communication interface 1460 for packet data communication. Theplatform also may include a central processing unit (CPU) 1420, in theform of one or more processors, for executing program instructions. Theplatform typically includes an internal communication bus 1410, programstorage, and data storage for various data files to be processed and/orcommunicated by the platform such as ROM 1430 and RAM 1440 or the like.The hardware elements, operating systems, and programming languages ofsuch equipment are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith. The platform 1400also may include input and output ports 1450 to connect with input andoutput devices such as keyboards, mice, touchscreens, monitors,displays, etc., and communication ports 1460. Of course, the variousserver functions may be implemented in a distributed fashion on a numberof similar platforms to distribute the processing load. Alternatively,the servers may be implemented by appropriate programming of onecomputer hardware platform.

It would be apparent to one of skill in the relevant art that thepresent disclosure, as described herein, can be implemented in manydifferent examples of software, hardware, firmware, and/or the entitiesillustrated in the figures. Any actual software code with thespecialized control of hardware to implement examples is not limiting ofthe detailed description. Thus, examples are described herein with theunderstanding that modifications and variations of the examples arepossible, given the level of detail presented herein. Aspects of thedescribed subject matter may be thought of as “products” or “articles ofmanufacture” typically in the form of executable code and/or associateddata that is carried on or embodied in a type of machine-readablemedium. “Storage” type media include any or all of the tangible memoryof the computers, processors or the like, or associated modules thereof,such as various semiconductor memories, tape drives, disk drives and thelike, which may provide non-transitory storage at any time for thesoftware programming. All or portions of the software may at times becommunicated through the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of the mobile communication networkinto the computer platform of a server and/or from a server to themobile device. Thus, another type of media that may bear the softwareelements includes optical, electrical and electromagnetic waves, such asused across physical interfaces between local devices, through wired andoptical landline networks and over various air-links. The physicalelements that carry such waves, such as wired or wireless links, opticallinks, or the like, also may be considered as media bearing thesoftware. As used herein, unless restricted to non-transitory, tangible“storage” media, terms such as computer or machine “readable medium”refer to any medium that participates in providing instructions to aprocessor for execution.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed examples, as claimed.

Other examples of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

As is evident from the figures, text, and examples presented above, avariety of embodiments may be contemplated including, but not limitedto:

1. A computer-implemented method for managing glucose states of a user,the method comprising:

receiving the user's glucose levels using a continuous glucosemonitoring (CGM) device;

determining a time in range (TIR) value of the user's glucose level,wherein the TIR value is based on an amount of time the user's glucoselevel is within a threshold band over a base time period;

determining a TIR state based on the TIR value;

receiving a glucose variability (GV) value based at least on the user'sglucose level, wherein the GV value is one of a standard deviation or acoefficient of variance (CV), wherein a CV indicates a variability ofthe user's glucose level in view of a standard deviation of the glucoselevel over the base time period;

determining a GV state based on the GV value;

determining a starting state based on the TIR state and the GV state;

determining that the starting state corresponds to a non-ideal state;

generating an optimized pathway to reach an ideal state based on one ormore user vectors and the starting state, the optimized pathwaycomprising one or more adjustments to the one or more user vectors; and

providing the optimized pathway to the user.

2. The method of embodiment 1, wherein the threshold band is betweenapproximately 70 mg/dL and 180 mg/dL.

3. The method of embodiment 1, wherein the base time period is 24 hours.

4. The method of embodiment 1, wherein the CV value is determined bydividing the standard deviation of the glucose level by a mean of theglucose level over the base time period.

5. The method of embodiment 1, wherein the TIR state is a binary stateselected form one of a good TIR state or a bad TIR state.

6. The method of embodiment 5, wherein the good TIR state corresponds toa TIR value of greater than a TIR cutoff.

7. The method of embodiment 1, wherein the GV state is a binary stateselected form one of a good GV state or a bad GV state.

8. The method of embodiment 7, wherein the good GV state corresponds toa GV value of greater than a GV cutoff.

9. The method of embodiment 1, wherein the user vectors comprise one ormore of medications, food consumption, exercise value, psycho-socialparameters, or social-determinant parameters.

10. The method of embodiment 1, further comprising:

classifying one or more CGM events based on the user's glucose levels,wherein the classifying is based at least on a severity score associatedwith each of the one or more CGM events; and

generating the optimized pathway further based on the classifying one ormore CGM events.

11. The method of embodiment 1, wherein the optimized pathway is furtherbased on a user attribute, the user attribute selected from one or moreof a social attribute, medical attribute, user preference, metabolicattribute, or user demographic.

12. The method of embodiment 1, wherein the optimized pathway comprisesan increase in one or more state improving habits and/or a decrease inone or more state worsening habits.

13. A computer-implemented method for managing glucose states of a user,the method comprising:

receiving a plurality of optimization profiles for reaching an idealstate from a non-ideal state, the ideal state corresponding to a goodtime in range (TIR) state and good a glucose variability (GV) state andthe non-ideal state comprising at least one of a bad TIR state or a badGV state;

determining a current TIR state based on a TIR value of the user'sglucose level, wherein the TIR value is based on an amount of time theuser's glucose level is within a threshold band over a base time periodand the current TIR state is one of a good TIR state or a bad TIR state;

determining a current GV state being based on a GV value associated withthe user's glucose level, wherein the GV value indicates a standarddeviation (SD) of glucose levels or a coefficient of variance (CV),wherein the CV is variability of the user's glucose level in view of astandard deviation of the glucose level over the base time period;

receiving one or more user vectors for the user;

identifying one of the optimization profiles based on the one or moreuser vectors and one or more user attributes;

identifying an optimized pathway based on the identified optimizationprofile, the TIR state, and the GV state, the optimized pathwaycomprising one or more adjustments to the one or more user vectors; and

providing the optimized pathway to the user.

14. The method of embodiment 13, wherein each of the plurality ofoptimization profiles comprise a different combination of a plurality ofuser vectors and a plurality of user attributes.

15. The method of embodiment 14, wherein the plurality of optimizationprofiles are each associated with a plurality of optimized pathways,each of the plurality of optimized pathways being identified based onone or more of a potential TIR state or a potential GV state.

16. The method of embodiment 13, wherein a machine learning modelreceives, as input, the optimization profile, the TIR state, and the GVstate to output the optimized pathway.

17. The method of embodiment 13, further comprising receiving one ormore user attribute and identifying one of the optimization profilesfurther based on the one or more user attributes.

18. The method of embodiment 13, wherein the CV value is determined bydividing the standard deviation of the glucose level by a mean of theglucose level over the base time period.

19. A system for managing glucose levels of a user, the systemcomprising:

a memory having processor-readable instructions stored therein; and

a processor configured to access the memory and execute theprocessor-readable instructions, which, when executed by the processorconfigures the processor to perform a method, the method comprising:

electronically receiving the user's glucose levels using a continuousglucose monitoring (CGM) device configured to obtain glucose valuesusing a component that penetrates a skin of the user;

determining a time in range (TIR) value of the user's glucose level,wherein the TIR value is based on an amount of time the user's glucoselevel is within a threshold band over a base time period wherein thethreshold band is between approximately 70 mg/dL and 180 mg/dL and thebase time period is 24 hours;

determining a TIR state based on the TIR value, wherein the TIR state isselected form a good TIR state or a bad TIR state;

receiving a glucose variability (GV) value based at least on the user'sglucose level, wherein the GV value is one of a standard deviation or acoefficient of variance (CV), wherein a CV indicates a variability ofthe user's glucose level in view of a standard deviation of the glucoselevel over the base time period;

determining a GV state based on the GV value, wherein the GV state isone of a good GV state or a bad GV state;

determining a starting state based on the TIR state and the GV state;

determining that the starting state corresponds to a non-ideal state;

detecting a CGM event based on the user's glucose levels;

characterizing the CGM event based on one or more of a multi-parameterCGM classification or a severity and CGM event trace shapecharacterization, wherein the multi-parameter CGM classificationcomprises a glucose level at a beginning of the CGM event, a severity,and a glucose at an end of the CGM event;

generating an optimized pathway to reach an ideal state based on one ormore account vectors and the characterizing the CGM event, the optimizedpathway comprising one or more adjustments to the one or more accountvectors; and

providing the optimized pathway to the user.

20. The system of embodiment 19, wherein providing the optimized pathwayto the user comprises providing context based instructions to the userbased on the optimized pathway.

Additional embodiments include:

1. A system for providing glucose trend based behavior outputs, thesystem comprising:

a continuous glucose monitoring (CGM) device configured to output aplurality of glucose readings based on analyzing a bodily fluid over aperiod of time;

a memory configured to store the plurality of glucose readings; and

a processor configured to:

determine a CGM trend based on a change in the plurality of glucosereadings output by the CGM device and/or stored in the memory;

determine at least one behavior output based on the CGM trend and atleast one additional factor; and

provide the at least one behavior output to a user.

2. The system of embodiment 1, wherein the CGM device is furtherconfigured to output a subsequent glucose reading, based on the bodilyfluid, after the period of time and wherein the processor is furtherconfigured to determine an updated CGM trend based on the subsequentglucose reading.

3. The system of embodiment 1, wherein the at least one behavior outputcorresponds to at least one behavior category selected from whetherinsulin is needed, how much insulin is needed, whether glucose isneeded, how much glucose is needed, whether food consumption is needed,how much food consumption is needed, whether exercise is needed, or howmuch exercise is needed.

4. The system of embodiment 3, wherein the at least one behavior outputcategories is selected based on a type of the one additional factor.

5. The system of embodiment 1, wherein the at least one additionalfactor comprises dietary information.

6. The system of embodiment 5, wherein the dietary information comprisesan insulin to carbohydrate ratio.

7. The system of embodiment 1, wherein the at least one additionalfactor comprises exercise information.

8. The system of embodiment 7, wherein the exercise information maycomprise at least one of caloric information, heart rate information,duration of exercise, intensity of exercise, or strain on body.

9. The system of embodiment 1, wherein the at least one additionalfactor comprises information regarding a previous insulin dose.

10. The system of embodiment 1, wherein the at least one additionalfactor comprises a glucose level.

11. The system of embodiment 1, wherein the at least one additionalfactor comprises information regarding a hypoglycemia history.

12. The system of embodiment 11, wherein a hypoglycemia episode within athreshold amount of time causes a behavior output in an insulinrecommendation behavior category to be more conservative in comparisonto the behavior output in the insulin recommendation behavior categorywithout the hypoglycemia episode within the threshold amount of time.

13. The system of embodiment 1, wherein the processor comprises amachine learning model configured to output the at least one behavioroutput based on one or more past behavior outputs and a correspondingchange in a past CGM trend.

14. The system of embodiment 1, wherein the at least one behavior outputis provided to the user using at least one of the CGM monitor, anelectronic device, or an application.

15. A computer-implemented method for providing glucose trend basedbehavior outputs, the method comprising:

receiving, from a continuous glucose monitor (CGM) device, a pluralityof glucose readings based on the CGM device analyzing a bodily fluidover a period of time;

determining a CGM trend based on a change in the plurality of glucosereadings output by the CGM device;

determining at least one behavior output based on the CGM trend; and

providing the at least one behavior output to a user.

16. The method of embodiment 15, wherein the at least one behavioroutput corresponds to at least one behavior category selected fromwhether insulin is needed, how much insulin is needed, whether glucoseis needed, how much glucose is needed, whether food consumption isneeded, how much food consumption is needed, whether exercise is needed,or how much exercise is needed.

17. The method of embodiment 15, wherein the CGM device is furtherconfigured to output a subsequent glucose reading, based on the bodilyfluid, after the period of time and further comprising determining anupdated CGM trend based on the subsequent glucose reading.

18. The method of embodiment 17, further comprising:

receiving the updated CGM trend at the processor;

determining at least one updated behavior output based on the updatedCGM trend; and

providing the at least one updated behavior output to a user.

19. A system for providing glucose trend based behavior outputs, thesystem comprising:

a continuous glucose monitoring (CGM) device configured to output aplurality of glucose readings based on analyzing a bodily fluid over aperiod of time, wherein the CGM device access the bodily fluid via auser's skin and wherein the CGM device is configured to obtain a glucosereading in increments of five minutes or less;

a memory configured to store the plurality of glucose readings; and

a processor configured to:

-   -   determine a CGM trend based on a change in the plurality of        glucose readings output by the CGM device and/or stored in the        memory, wherein the CGM trend is determined using a CGM trace        mapping the glucose readings over a period of time, and wherein        the CGM trend is further based on at least one of a CGM event or        a severity score;    -   receiving at least one additional factor, wherein the at least        one additional factor comprises one or more of dietary        information, exercise information, an insulin to carbohydrate        ratio, information regarding a previous insulin dose, a glucose        level, and information regarding a hypoglycemia history;

identifying at least one behavior category selected from whether insulinis needed, how much insulin is needed, whether glucose is needed, howmuch glucose is needed, whether food consumption is needed, how muchfood consumption is needed, whether exercise is needed, or how muchexercise is needed, based on the CGM trend and the at least oneadditional factor;

determine at least one behavior output based on the CGM trend and the atleast one additional factor, wherein the at least one behavior output isfrom the at least one identified behavior category and wherein the atleast one behavior output is determined using a machine learning modelconfigured to output the at least one behavior output based on one ormore past behavior outputs and a corresponding change in a past CGMtrend;

generating a graphical user interface (GUI) based on the at least oneidentified behavior category;

providing the at least one behavior output to a user via the generatedGUI;

receiving an updated CGM trend after providing the at least one behavioroutput to the user, wherein the update CGM trend is based on glucosereadings after providing the at least one behavior output to the user;and

updating the machine learning model based on the updated CGM trend.

20. The system of embodiment 19, further comprising:

providing the updated CGM trend as an input to the insulin computer;

determining, by the insulin computer, at least one updated behavioroutput based on the updated CGM trend; and

providing the at least one updated behavior output to a user.

Additional embodiments include:

1. A system for managing glucose states of a user, the systemcomprising:

a continuous glucose monitoring (CGM) device configured to output aplurality of glucose readings based on analyzing a bodily fluid over aperiod of time;

a memory configured to store the plurality of glucose readings; and

a processor configured to:

generate a CGM trace based on the plurality of glucose readings over theperiod of time;

identify a severity score of the CGM trace, wherein the severity scoreis based on a height of the CGM trace and a duration of time that theCGM trace stays above a target value;

identify a starting state based on the severity score, the startingstate being indicative of a glucose health of the user;

generate an optimized pathway to reach an ideal state based on one ormore user vectors and the starting state, the optimized pathwaycomprising one or more adjustments to the one or more user vectors; and

provide the optimized pathway to the user.

2. The system of embodiment 1, further comprising:

identifying a beginning parameter, wherein the beginning parameter is ascaled value determined based on a beginning point of the CGM trace incomparison to a target range; and

generating the optimized pathway based further on the beginningparameter.

3. The system of embodiment 2, wherein the beginning parameter isselected form one of a very high parameter, a high parameter, an inrange parameter, a low parameter, and a very low parameter.

4. The system of embodiment 1, further comprising:

identifying an ending parameter, wherein the ending parameter is ascaled value determined based on an ending point of the CGM trace incomparison to a target range;

and

generating the optimized pathway based further on the ending parameter.

5. The system of embodiment 1, wherein the severity score is determinedby multiplying a height of the CGM trace by a duration that the CGMtrace is above the target value.

6. The system of embodiment 5, wherein the height of the CGM trace isgiven a first weight and the duration that the CGM trace is above thetarget value is given a second weight different than the first weight.

7. The system of embodiment 1, wherein a lower severity scorecorresponds to a starting state closer to the ideal state when comparedto a higher severity score.

8. The system of embodiment 1, wherein the user vectors comprise one ormore of medications, food consumption, exercise value, psycho-socialparameters, or social-determinant parameters.

9. The system of embodiment 1, wherein the optimized pathway is selectedfrom an optimization profile and wherein the optimization profile isidentified based on the severity score and one or more usercharacteristics.

10. The system of embodiment 1, further comprising:

determining a time in range (TIR) value of the CGM trace, wherein theTIR value is based on an amount of time the CGM trace is within athreshold band over a base time period;

determining a TIR state based on the TIR value;

receiving a glucose variability (GV) value based at least on the CGMtrace, wherein the GV value is one of a standard deviation or acoefficient of variance (CV), wherein a CV indicates a variability ofthe glucose readings in view of a standard deviation of the glucosereadings over the base time period;

determining a GV state based on the GV value; and

determining the starting state further based on the TIR state and the GVstate.

11. A computer-implemented method for managing glucose states of a user,the method comprising:

receiving glucose readings of the user, over a period of time, from acontinuous glucose monitoring (CGM) device;

generating a CGM trace based on the received glucose readings;

identifying a severity score of the CGM trace, wherein the severityscore is based on a height of the CGM trace and a duration of time thatthe CGM trace stays above a target value;

identifying a CGM trace shape of the CGM trace, wherein the CGM traceshape is based on at least one of a height or a width of a CGM trace;

identifying a starting state based on the severity score and the CGMtrace shape, the starting state being indicative of a glucose health ofthe user;

generating an optimized pathway to reach an ideal state based on one ormore user vectors and the starting state, the optimized pathwaycomprising one or more adjustments to the one or more user vectors; and

providing the optimized pathway to the user.

12. The method of embodiment 11, wherein the CGM trace shape is one of awide shape, a narrow shape, a short shape, and a tall shape.

13. The method of embodiment 12, wherein the CGM trace shape isidentified by a machine learning model configured to output CGM traceshapes based on the CGM trace.

14. The method of embodiment 13, wherein the machine learning model maybe configured to output CGM trace shapes based on past CGM trace shapes.

15. A system for managing glucose states of a user, the systemcomprising:

a continuous glucose monitoring (CGM) device configured to output aplurality of glucose readings based on analyzing a bodily fluid over aperiod of time, wherein the CGM device access the bodily fluid via auser's skin and wherein the CGM device is configured to obtain a glucosereading in increments of five minutes or less;

a memory configured to store the plurality of glucose readings; and

a processor configured to:

generate a CGM trace mapping the glucose readings over a period of time;

identify a severity score of the CGM trace, wherein the severity scoreis based on a height of the CGM trace and a duration of time that theCGM trace stays above a target value;

identifying a CGM trace shape of the CGM trace using a machine learningmodel, wherein the CGM trace shape is based on at least one of a heightor a width of a CGM trace;

identify a starting state based on the severity score and the CGM traceshape, the starting state being indicative of a glucose health of theuser;

generate an optimized pathway to reach an ideal state based on one ormore user vectors and the starting state, the optimized pathwaycomprising one or more adjustments to the one or more user vectors;

generating a graphical user interface (GUI) based on the optimizedpathway;

providing the at least one optimized pathway to a user via the generatedGUI;

receiving an updated CGM trace after providing the optimized pathway tothe user, wherein the update CGM trace is based on glucose readingsafter providing the optimized pathway to the user; and

updating the machine learning model based on the updated CGM trace.

16. The system of embodiment 15, further comprising:

identifying a beginning parameter, wherein the beginning parameter is ascaled value determined based on a beginning point of the CGM trace incomparison to a target range; and

generating the optimized pathway based further on the beginningparameter.

17. The system of embodiment 16, wherein the beginning parameter isselected form one of a very high parameter, a high parameter, an inrange parameter, a low parameter, and a very low parameter.

18. The system of embodiment 15, further comprising:

identifying an ending parameter, wherein the ending parameter is ascaled value determined based on an ending point of the CGM trace incomparison to a target range;

and

generating the optimized pathway based further on the ending parameter.

19. The system of embodiment 15, wherein the CGM trace shape is one of awide shape, a narrow shape, a short shape, and a tall shape.

20. The system of embodiment 15, wherein the user vectors comprise oneor more of medications, food consumption, exercise value, psycho-socialparameters, or social-determinant parameters.

What is claimed is:
 1. A computer-implemented method for managingglucose states of a user, the method comprising: receiving a pluralityof optimization profiles for reaching an ideal state from a non-idealstate, the ideal state corresponding to a good time in range (TIR) stateand a good glucose variability (GV) state and the non-ideal statecomprising at least one of a bad TIR state or a bad GV state, whereinthe good TIR state is above a threshold TIR state and a bad TIR state isbelow the threshold TIR state; determining a current TIR state based ona TIR value of the user's glucose level over a first period of time,wherein the TIR value is based on an amount of time the user's glucoselevel is within a threshold band over a base time period and the currentTIR state is one of a good current TIR state or a bad current TIR state;determining a current GV state being based on a GV value associated withthe user's glucose level, wherein the GV value indicates a standarddeviation (SD) of glucose levels or a coefficient of variance (CV),wherein the CV is variability of the user's glucose level in view of astandard deviation of the glucose level over the base time period;receiving one or more user vectors for the user; identifying one of theoptimization profiles based on the one or more user vectors and one ormore user attributes; identifying an optimized pathway based on theidentified optimization profile, the TIR state, and the GV state, theoptimized pathway comprising one or more adjustments to the one or moreuser vectors, wherein the one or more adjustments comprise a medicationadjustment, a food consumption adjustment, and an exercise value,wherein: the optimized pathway is provided as a machine learning modeloutput, the machine learning model output comprising a user vectorchange based on machine learning inputs comprising the TIR state and theGV state; the machine learning model inputs further comprising userattributes, wherein the user attributes comprise a medical attribute, auser preference, a metabolic attribute, and a user demographic; theoptimized pathway is further based on a habit index score of the user,determined based on a cohort of users with one or more user attributesin common with the user; and providing the optimized pathway to theuser.
 2. The method of claim 1, wherein each of the plurality ofoptimization profiles comprise a different combination of a plurality ofuser vectors and a plurality of user attributes.
 3. The method of claim2, wherein the plurality of optimization profiles are each associatedwith a plurality of optimized pathways, each of the plurality ofoptimized pathways being identified based on one or more of a potentialTIR state or a potential GV state.
 4. The method of claim 1, furthercomprising: receiving updated glucose levels over a second period oftime; identifying an updated optimized pathway to reach the ideal statebased on the updated glucose levels, the updated optimized pathwaycomprising insulin intake information; and providing the updatedoptimized pathway to the user.
 5. The method of claim 1, wherein theoptimized pathway is provided to the user via a graphical userinterface.
 6. The method of claim 1, wherein the machine learning modelis trained using one of a supervised learning, unsupervised learning, orsemi-supervised learning.
 7. The method of claim 1, further comprisingreceiving one or more user attribute and identifying one of theoptimization profiles further based on the one or more user attributes.8. The method of claim 1, wherein the CV value is determined by dividingthe standard deviation of the glucose level by a mean of the glucoselevel over the base time period.
 9. The method of claim 1, wherein theuser's glucose levels are determined by a continuous glucose monitoring(CGM) device configured to obtain bodily fluid via a skin penetratingcomponent.
 10. The method of claim 9, wherein the CGM device isconfigured to determine the user's glucose levels by sensing aconcentration of analytes within the obtained bodily fluid.
 11. A systemfor managing glucose states of a user, the system comprising: at leastone memory storing instructions; a continuous glucose monitoring (CGM)device configured to obtain bodily fluid via a skin penetratingcomponent; and at least one processor executing the instructions toperform operations, the operations comprising: receiving a plurality ofoptimization profiles for reaching an ideal state from a non-idealstate, the ideal state corresponding to a good time in range (TIR) stateand a good glucose variability (GV) state and the non-ideal statecomprising at least one of a bad TIR state or a bad GV state, whereinthe good TIR state is above a threshold TIR state and a bad TIR state isbelow the threshold TIR state; determining a current TIR state based ona TIR value of the user's glucose level over a first period of time,wherein the TIR value is based on an amount of time the user's glucoselevel is within a threshold band over a base time period and the currentTIR state is one of a good current TIR state or a bad current TIR state;determining a current GV state being based on a GV value associated withthe user's glucose level, wherein the GV value indicates a standarddeviation (SD) of glucose levels or a coefficient of variance (CV),wherein the CV is variability of the user's glucose level in view of astandard deviation of the glucose level over the base time period;receiving one or more user vectors for the user; identifying one of theoptimization profiles based on the one or more user vectors and one ormore user attributes; identifying an optimized pathway based on theidentified optimization profile, the TIR state, and the GV state, theoptimized pathway comprising one or more adjustments to the one or moreuser vectors, wherein the one or more adjustments comprise a medicationadjustment, a food consumption adjustment, and an exercise value,wherein: the optimized pathway is provided as a machine learning modeloutput, the machine learning model output comprising a user vectorchange based on machine learning inputs comprising the TIR state and theGV state; the machine learning model inputs further comprising userattributes, wherein the user attributes comprise a medical attribute, auser preference, a metabolic attribute, and a user demographic; theoptimized pathway is further based on a habit index score of the user,determined based on a cohort of users with one or more user attributesin common with the user; and providing the optimized pathway to theuser.
 12. The system of claim 11, wherein each of the plurality ofoptimization profiles comprise a different combination of a plurality ofuser vectors and a plurality of user attributes.
 13. The system of claim12, wherein the plurality of optimization profiles are each associatedwith a plurality of optimized pathways, each of the plurality ofoptimized pathways being identified based on one or more of a potentialTIR state or a potential GV state.
 14. The system of claim 11, whereinthe processor is further configured to: receiving updated glucose levelsover a second period of time; identifying an updated optimized pathwayto reach the ideal state based on the updated glucose levels, theupdated optimized pathway comprising insulin intake information; andproviding the updated optimized pathway to the user.
 15. The system ofclaim 11, wherein the optimized pathway is provided to the user via agraphical user interface.
 16. The system of claim 11, wherein themachine learning model is trained using one of a supervised learning,unsupervised learning, or semi-supervised learning.
 17. The system ofclaim 11, further comprising receiving one or more user attribute andidentifying one of the optimization profiles further based on the one ormore user attributes.
 18. The system of claim 11, wherein the CV valueis determined by dividing the standard deviation of the glucose level bya mean of the glucose level over the base time period.
 19. The system ofclaim 18, wherein the CGM device is configured to determine the user'sglucose levels by sensing a concentration of analytes within theobtained bodily fluid.
 20. A non-transitory computer-readable mediumstoring instructions that, when executed by a processor, cause theprocessor to perform operations, the operations comprising: receiving aplurality of optimization profiles for reaching an ideal state from anon-ideal state, the ideal state corresponding to a good time in range(TIR) state and good glucose variability (GV) state and the non-idealstate comprising at least one of a bad TIR state or a bad GV state,wherein the good TIR state is above a threshold TIR state and a bad TIRstate is below the threshold TIR state; determining a current TIR statebased on a TIR value of the user's glucose level over a first period oftime, wherein the TIR value is based on an amount of time the user'sglucose level is within a threshold band over a base time period and thecurrent TIR state is one of a good current TIR state or a bad currentTIR state; determining a current GV state being based on a GV valueassociated with the user's glucose level, wherein the GV value indicatesa standard deviation (SD) of glucose levels or a coefficient of variance(CV), wherein the CV is variability of the user's glucose level in viewof a standard deviation of the glucose level over the base time period;receiving one or more user vectors for the user; identifying one of theoptimization profiles based on the one or more user vectors and one ormore user attributes; identifying an optimized pathway based on theidentified optimization profile, the TIR state, and the GV state, theoptimized pathway comprising one or more adjustments to the one or moreuser vectors, wherein the one or more adjustments comprise a medicationadjustment, a food consumption adjustment, and an exercise value,wherein: the optimized pathway is provided as a machine learning modeloutput, the machine learning model output comprising a user vectorchange based on machine learning inputs comprising the TIR state and theGV state; the machine learning model inputs further comprising userattributes, wherein the user attributes comprise a medical attribute, auser preference, a metabolic attribute, and a user demographic; theoptimized pathway is further based on a habit index score of the user,determined based on a cohort of users with one or more user attributesin common with the user; and providing the optimized pathway to theuser.